Data Analysis for Underwater Environmental Acoustic

Data Analysis for Underwater Environmental Acoustic Monitoring Technologies

Contractor Report

Joe Hood Ben Bougher Akoostix Inc. Hilary Moors Phil Webster Independent Consultants Prepared By: Akoostix Inc. 10 Akerley Blvd - Suite 12 Dartmouth NS B3B 1J4

Contractor's Document Number: AI CR 2012-011 Contract Project Manager: Joe Hood, 902-404-7464 PWGSC Contract Number: W7707-4500910423 CSA: James Theriault, Defence Scientist, 902-426-3100 ext. 376 The scientific or technical validity of this Contract Report is entirely the responsibility of the Contractor and the contents do not necessarily have the approval or endorsement of Defence R&D Canada.

Contract Report

DRDC-RDDC-2017-C087

March 2012

Principal Author

Original signed by Joe Hood

Joe Hood

President

Approved by

Original signed by James Theriault

James Theriault

Defence Scientist

Approved for release by

Original signed by Calvin Hyatt

Calvin Hyatt

Chair, Document Review Panel

© Her Majesty the Queen in Right of Canada, as represented by the Minister of National Defence, 2012

© Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale,

2012

i

Abstract ....

Knowledge of cetacean distribution and abundance on the Scotian Shelf is limited and therefore

planning of naval exercises that attempt to mitigate the impact of operations on marine mammals,

especially cetaceans, is difficult. There are a number of barriers to improving knowledge some of

which may be overcome with better approaches, sensor systems, and processes. The objective of

this study was to examine the effectiveness of three types of acoustic sensor systems for detection

of cetacean species typically found on the Scotian Shelf: autonomous underwater vehicles

(AUV), floating sensors that broadcast acoustic data (sonobuoys), and bottom-moored sensor

systems. Data was collected using each of the sensor systems during the winter of 2012.

However, no AUV data was processed under this contract as it was collected near the completion

of the contract. Data were processed to determine variation in ambient-noise level and for

presence of cetacean vocalizations using a combination of automated and manual methods. The

effectiveness of these methods and the sensor systems were examined, but conclusions were

limited and no high-confidence cetacean vocalizations were found in the data. Since the data was

collected in February/March, no cetaceans were expected to be in the area. The team also

conducted a broader analysis, considering naval operations, biological factors, and sensor system

capability in order to recommend an approach for enhancing DND’s ability to improve on current

knowledge and procedures. Primary recommendations include a long-term cetacean monitoring

plan and additional capability development to process the high volume of data that would result

for valuable information.

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iii

Executive summary

Data Analysis for Underwater Environmental Acoustic Monitoring Technologies: Contractor Report

Joe Hood; Ben Bougher; Hilary Moors; Phil Webster; March 2012.

Introduction: Knowledge of cetacean distribution and abundance on the Scotian Shelf is limited

and therefore planning of naval exercises that attempt to mitigate the impact of operations on

marine mammals, especially cetaceans, is difficult. There are a number of barriers to improving

knowledge some of which may be overcome with better approaches, sensor systems, and

processes. The objective of this study was to examine the performance of three types of acoustic

sensor systems for detection of cetacean species typically found on the Scotian Shelf. The study

considered floating autonomous sensors (sonobuoys), autonomous underwater vehicles (AUV),

floating and bottom-moored sensor systems. Data was collected using each of the sensor systems

during the winter of 2012. However, AUV data was not processed under this contract as it was

collected near the completion of the contract

Results: Data were processed to determine variation in ambient-noise level and for presence of

cetacean vocalizations using a combination of automated and manual methods. The effectiveness

of these methods and the sensor systems were examined, but conclusions were limited and no

cetacean vocalizations were found in the data. Since the data was collected in February and

March, no cetaceans were expected to be in the area.

The study includes consideration of each of the sensor system capabilities in context of naval

operations impact mitigation requirements and cetacean characteristics. Primary recommendations

include a long-term cetacean monitoring plan and additional capability development to process

the high volume of data that would result for valuable information

Significance: This study constitutes the start of a careful and methodical consideration of the

Royal Canadian Navy’s needs for underwater acoustic monitoring for the purposes of mitigating

the potential impact of its operations on the marine environment.

Future plans: The acoustic AUV data collected in the experimental program was not analyzed as

part of this contract as the data collection was delayed beyond the completion of the contracted

analysis effort. This data needs to be analyzed for completion. Also, the experimental program

schedule was chosen such that the likelihood of interaction with cetaceans was minimal. Further

data collection at a time and location with higher interaction likelihood is needed.

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v

Table of contents

Abstract .... ....................................................................................................................................... i

Executive summary ........................................................................................................................ iii

Table of contents ............................................................................................................................. v

List of figures ............................................................................................................................... viii

List of tables ................................................................................................................................... ix

Acknowledgements ........................................................................................................................ xi

1 Introduction ............................................................................................................................... 1

2 Glider 2012 trial overview ........................................................................................................ 3

2.1 Sensor platform characteristics ...................................................................................... 3

2.1.1 AN/SSQ-53F sonobuoy .................................................................................. 3

2.1.2 SHARP recorders ............................................................................................ 4

2.1.3 Passive acoustic reusable buoy (PARB) ......................................................... 4

2.1.4 Glider Autonomous Underwater Vehicle ........................................................ 4

2.2 Trial data ........................................................................................................................ 5

3 Data processing ......................................................................................................................... 6

3.1 Data formatting .............................................................................................................. 6

3.2 Ambient-noise processing ............................................................................................. 7

3.3 Automated detection of transients and cetacean vocalizations ...................................... 7

3.3.1 Transient .......................................................................................................... 9

3.3.2 Sperm whale .................................................................................................... 9

3.3.3 Fin Whale ...................................................................................................... 10

3.3.4 Humpback whale ........................................................................................... 10

3.3.5 Minke whale .................................................................................................. 10

3.3.6 Sei whale ....................................................................................................... 11

3.3.7 General squeals ............................................................................................. 11

3.3.8 Delphinids ..................................................................................................... 12

4 Data analysis ........................................................................................................................... 13

4.1 Ambient analysis ......................................................................................................... 13

4.2 Detection analysis ........................................................................................................ 15

5 Passive acoustic monitoring (PAM) options........................................................................... 26

5.1 Technologies reviewed ................................................................................................ 26

5.1.1 Real-time floating analog sensors ................................................................. 26

5.1.2 Acoustic sensors onboard autonomous underwater vehicles ........................ 27

5.1.3 Bottom-moored acoustic systems ................................................................. 27

5.2 Operational issues ........................................................................................................ 28

5.2.1 Acoustic monitoring locations ...................................................................... 29

vi

5.2.2 Duration of acoustic monitoring ................................................................... 31

5.2.3 Timing of acoustic monitoring ...................................................................... 31

5.2.4 Acoustic security ........................................................................................... 31

5.2.5 Deployment and retrieval of sensors ............................................................. 32

5.2.6 Mutual interference ....................................................................................... 32

5.2.7 Recording frequency range ........................................................................... 33

5.2.8 Data transfer rate ........................................................................................... 33

5.2.9 Acoustic analysis ........................................................................................... 34

5.2.10 Cost ............................................................................................................... 34

5.3 Biological considerations ............................................................................................ 34

5.3.1 Target species ................................................................................................ 34

5.3.2 Distribution and abundance of target species ................................................ 35

5.3.3 Residency of target species ........................................................................... 35

5.3.4 Vocal behaviour of target species ................................................................. 38

6 Discussion and recommendations ........................................................................................... 40

6.1 Monitoring plans ......................................................................................................... 40

6.1.1 Long-term monitoring ................................................................................... 40

6.1.2 Monitoring for specific exercises .................................................................. 41

6.2 Exercise planning ........................................................................................................ 41

6.3 System/analysis tools and process development ......................................................... 41

6.3.1 Detector testing and improvement ................................................................ 42

6.3.2 False alarm reduction .................................................................................... 42

6.3.3 ACDC software application .......................................................................... 43

6.3.4 Software sonobuoy GPS decoder .................................................................. 43

6.3.5 Ambient-noise monitoring ............................................................................ 43

7 Summary and conclusions ...................................................................................................... 44

References ..... ............................................................................................................................... 45

Annex A.... Cetacean detection recommendations ......................................................................... 49

A.1 Species most likely to be detected ............................................................................... 49

A.2 Detector configuration ................................................................................................. 50

Annex B .... Software Tools............................................................................................................. 55

B.1 Signal Processing Packages (SPPACS) ....................................................................... 55

B.1.1 Background and design information ............................................................. 56

B.2 STAR-IDL ................................................................................................................... 56

B.3 Omni-Passive Display (OPD) ...................................................................................... 57

B.4 Acoustic Cetacean Detection Capability (ACDC) ...................................................... 58

B.5 Acoustic Subsystem (AS) ............................................................................................ 58

B.6 Passive Acoustic Reusable Buoy (PARB) ................................................................... 59

Annex C .... Software enhancements ............................................................................................... 61

Annex D.... Configuration management ......................................................................................... 63

vii

D.1 STAR branch and release information ........................................................................ 63

D.1.1 STAR software documentation ..................................................................... 63

D.2 Issue summary ............................................................................................................. 64

List of symbols/abbreviations/acronyms/initialisms ..................................................................... 69

viii

List of figures

Figure 1: Block diagram of the processing stream used to generate 5-minute 1.0 Hz ambient-

noise spectra and 3rd

-octave spectra. ............................................................................. 7

Figure 2: DRDC’s automated transient detection stream. Refer to the online SPPACS help for

each application in bold to determine the available options for each program. ............ 9

Figure 3: Plot of statistical analysis of spectral levels for approximately two hours of data

from one AN/SSQ-53F deployed on 23 Feb 2012 (15:01Z to 16:55Z). The strong

signals between 500 Hz and 1000 Hz are likely due to the trial vessel. ...................... 14

Figure 4: Plot of statistical analysis of third-octave average levels for approximately two

hours of data from one AN/SSQ-53F deployed on 23 Feb 2012 (15:01Z to

16:55Z). The strong signals between 500 Hz and 1000 Hz are less prominent than

for Figure 3. ................................................................................................................. 14

Figure 5: Example of a Detection Summary Plot showing when detections occurred for each

detector type. Green bars indicate periods for which recordings were obtained, red

bars would indicate periods where no recordings were obtained (not present in this

case). Each triangle marks the number of detections obtained within a one-minute

period. The total number of detections obtained from each detector is given in

parenthesis to the right of the detector configuration name. ....................................... 16

Figure 6: Causes of false alarms determined from detailed manual analysis of three sets of

recordings (examples from each of the three recordings systems used). .................... 25

Figure 7: MARLANT operational areas. ....................................................................................... 30

Figure 8: Distribution for Species at Risk in and adjacent to the MARLOAs based on

sightings data currently available (taken from MOAMP). .......................................... 36

Figure 9: Distribution for other species in and adjacent to the MARLOAs based on sightings

data currently available (taken from MOAMP). ......................................................... 37

Figure 10: Spectrum of Atlantic species of interest. Note: vocalizations have not been

published for Sowerby’s, Dwarf sperm whales, and True’s beaked whales. .............. 39

Figure A-11: Spectrogram of downward sweeps likely produced by sei whales (Baumgarter

et al. 2008) ................................................................................................................... 51

ix

List of tables

Table 1: Summary of data collected during Glider 2012 ................................................................ 5

Table 2: Transient-detection settings ............................................................................................... 9

Table 3: Sperm-whale-detector settings .......................................................................................... 9

Table 4: Fin-whale-detector settings ............................................................................................. 10

Table 5: Humpback-whale-detector settings ................................................................................. 10

Table 6: Minke-whale-detector settings ........................................................................................ 10

Table 7: Sei-whale-detector settings.............................................................................................. 11

Table 8: Detector settings for squeals............................................................................................ 11

Table 9: Detector settings for delphinid clicks .............................................................................. 12

Table 10: Identification of false detections from Detection Summary Plots, example shown

for sonobuoy recordings from 22 Feb 2012. ............................................................... 17


for PARB recordings from 23 Feb 2012. .................................................................... 17


for SHARP recordings from 24 Feb 2012. .................................................................. 18

Table 13: Status of manual analysis .............................................................................................. 18

Table 14: Manual analysis effort where the detections were analyzed in detail, example

shown for sonobuoy recordings from 22 Feb 2012. .................................................... 21


shown for PARB recordings from 23 Feb 2012. ......................................................... 22


shown for SHARP recordings from 24 Feb 2012. ...................................................... 22

Table 17: Detector performance determined from detailed manual analysis of recordings,

example shown for sonobuoy recordings from 22 Feb 2012. ..................................... 23


example shown for PARB recordings from 23 Feb 2012. .......................................... 23


example shown for SHARP recordings from 24 Feb 2012 ......................................... 24

Table 20: Operational considerations related to the four PAM technologies that were

reviewed. ..................................................................................................................... 28

Table A-21: Assessment of whether cetacean species that frequently occur on the Scotian

Shelf region are likely to be recorded in February/March in the study area. .............. 49

Table A-22: Description of vocalizations made by the cetacean species that are most likely to

be recorded in February/March in the study area ........................................................ 52

x

Table D-23: Issue summary (severity vs. status) for all software on STAR release 6.6.11 .......... 64

Table D-24: Known critical issues for STAR ............................................................................... 65

xi

Acknowledgements

Akoostix would like to acknowledge the efforts of the Defence Research and Development

Canada (DRDC) Glider 2012 sea-trial team, for whom the technical lead was Timothy Murphy.

The data analyzed under this trial would not have been gathered without their efforts, care, and

diligence in harsh winter North Atlantic waters.

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1

1 Introduction

This contractor report documents work performed under contract W7707-4500910423 for Project

Authority (PA), James Theriault. The work was conducted between February and March 2012.

DRDC Atlantic was tasked by MARLANT Formation Safety and Environment (FSE) to evaluate

three passive acoustic monitoring (PAM) technologies for their effectiveness in monitoring for

the presence of marine mammals (especially cetaceans) before, during and after naval exercises.

In support of this tasking, DRDC planned the Glider 2012 sea trial to occur east of the Halifax,

Nova Scotia harbour approaches. A DRDC team onboard a chartered vessel would transit to the

area, deploying and recovering equipment and data each day for a one to two week period.

During this trial, three technologies would be deployed to monitor for cetacean species that may

be present. The three planned technologies were:

Free-floating AN/SSQ-35F sonobuoys

Bottom-moored Subsurface High-fidelity Audio-Recording Package (SHARP) acoustic

recorders

Autonomous mobile Teledyne-WRC Slocum Gliders

DRDC also deployed passive acoustic reusable buoys (PARB) during initial trips, as there were

serviceability issues with the Slocum Gliders. The issues were later resolved.

Akoostix was tasked analyze the Glider 2012 trial data searching for transients and cetacean

detections, examining automated detector performance and ambient-noise levels, and analyzing

passive acoustic monitoring (PAM) options that might be appropriate for the Royal Canadian

Navy (RCN) to use in support of their environmental monitoring and mitigation process.

The contracted effort was very successful, completing automated processing within 24 hours of

receiving data from each of four datasets. These automated results were then analyzed by a

trained cetacean analyst to determine detector performance. During these efforts a focused team

of systems engineers, cetacean experts and experts in naval operations considered how these

technologies might best be exploited to support the RCN. Though no actual cetacean

vocalizations were recorded, much was learned and the software and processes used for this

contract were reused for similar work (Hood et al. 2012). Akoostix plans to continue to develop

these processes and complementary technology in collaboration with DRDC and the Department

of Fisheries and Oceans (DFO) Canada.

This report documents the data gathering and analysis effort and is organized as follows:

Section 2 provides an overview of the Glider 2012 sea trial, from which data was provided

to Akoostix for analysis under this contract.

Section 3 describes the methods used to process these data including formatting, ambient-

noise analysis, and automated detection processing.

Section 4 describes the analysis used to examine the processing results, including automated

detection performance.

2

Section 5 examines the technology options for PAM, including a discussion of operational

constraints and considerations.

Section 6 documents recommendations for future monitoring plans, exercise planning, and

system/analysis process development.

Section 7 summarizes the results of this work.

Annex A provides more detail on cetacean vocalizations that were studied in order to select

detector configurations.

0 provides a brief description of the software used to support this project.

Annex C provides a description of the software enhancements implemented during this

contract.

Annex D provides configuration management (CM) information to help users understand

which version of the software was used for this work.

3

2 Glider 2012 trial overview

The Glider 2012 trial was executed by DRDC during February 2012. The trial was executed as a

number of day trips to local waters off the mouth of Halifax harbour. (Trial reconstructions were

not performed under this contract and so maps including specific deployment locations are not

available.) A local charter company was contracted to provide a vessel, while DRDC

technologists, led by Timothy Murphy, made up the scientific team. Each trial day the team left

Halifax early in the morning. Once on-station they deployed a mix of:

AN/SSQ-53F sonobuoys in calibrated omni-directional (CO) mode

SHARP bottom-moored acoustic recorder units

PARB

DRDC intended to deploy Slocum Gliders, but a maintenance issue related to the depth sensor

prevented their use. A general description of each sensor platform is provided in Section 2.1.

Data were recorded during sensor deployment and then transferred to a hard drive upon recovery.

Sonobuoys were scuttled at the end of the day, while SHARP and PARB units were recovered

and recharged for future use.

Five day trips were conducted resulting in a large volume of data, as described in Section 2.2.

2.1 Sensor platform characteristics

2.1.1 AN/SSQ-53F sonobuoy

AN/SSQ-53F are standard sonobuoys that operate in three modes: DIFAR, constant shallow omni

(CSO), and calibrated omni-directional (CO), and can be set to operate for up to eight hours

before self-scuttling. Buoys were operated in CO mode for this trial because CO mode provides

the greatest bandwidth, covering 10 Hz to 40 kHz, though calibration is only for 10 Hz to 20 kHz.

These sensors transmit acoustic time series as analogue data on a very high frequency (VHF)

frequency modulated (FM) radio link that provides approximately 40 dB of dynamic range. Data

were received using an ICOM PCR2500 radio receiver and recorded using DRDC’s

environmental acoustic data acquisition (EADAQ) system. EADAQ was set to record 16-bit

signed data, covering the range ±10 Volts, sampled at 99 996.76 Hz. Data are stored in Defence

Research Establishment Atlantic (DREA) DAT formatted files.

EADAQ channel 1 was used to record a 1.5 kHz 1.0 Vrms sine wave, while channel 2 recorded a

GPS pulse-per-second (PPS) signal. Sonobuoy data were recorded on channel 3 and 4.

Some of the sonobuoys were fitted with GPS, but real-time GPS decode proved problematic. It

may be possible to post-process data files to extract GPS data, as described in Section 6.3.4.

4

2.1.2 SHARP recorders

The SHARP were developed by DRDC, containing a commercial off-the-shelf (COTS) Sony

PCM-D50 recording unit in a pressure casing with batteries, allowing it to operate for several

days. SHARP version 3 was used for this trial. The SHARP recorder was configured to store data

in 2-channel 24-bit WAV files, sampled at 44 100 Hz, though 16-bit and 24-bit resolution may be

used at standard sample rates between 22 050 Hz and 96 000 Hz. The recorder is connected to

two omni-directional hydrophones that are attached to the mooring line. This line has a weight on

one end to anchor it to the bottom and a float on the other to provide positive buoyancy and

suspend the hydrophones and recording equipment in the water column. The top hydrophone is

recorded on the left data channel, while the bottom hydrophone is recorded on the right data

channel. The SHARP unit can be deployed at any depth as long as the pressure vessel does not go

below 100 m depth. For Glider 2012 the bottom hydrophone was located 87 meters above the

anchor, while the top hydrophone is 107 meters above the anchor. The actual depth of the

hydrophone can vary due to currents, and depth is logged electronically. The recording period

varies depending on the sampling rate and duty-cycle used, and is typically constrained by the

storage capacity of the system (32 GB). These systems can generally record for a few days at a

time. For this trial, the recorders were set to record for 592 seconds1, every thirty minutes.

2.1.3 Passive acoustic reusable buoy (PARB)

Two different PARB units, named LP3 and LP4, were used during the trial, though only one was

deployed at a time. PARB are digital floating buoys with sensors deployed on a cable, similar to

sonobuoys. PARB contain a computer and storage, providing onboard processing and digital

recording. An omni-directional hydrophone was suspended on a 65 m cable, including 15 m of

suspension components, designed to decouple the hydrophone from surface motion.

The ambient recording function was used for this trial. In this mode, the system was set to record

30-minutes of data per file. There is a 7-second gap between the end of one file and the start of

the next, as the system closes off recording and prepares to start recording the next file.2 Data are

stored in single-channel 16-bit WAV files, sampled at 100 000 Hz. In this mode, a PARB could

record for approximately 4 days.

More detail on the PARB is provided in 0.

2.1.4 Glider Autonomous Underwater Vehicle

The Slocum Glider is a ship-deployable, variable-buoyancy underwater vehicle intended to

support a variety of sensors. After deployment, the glider 'flies' between pre-programmed depths

and GPS waypoints in a saw tooth path by changing buoyancy. The glider patrols a

predetermined surveillance area until a 'trip-wire' event (e.g., elapsed time, sensor inputs, etc.)

causes it to rise to the surface to establish contact with a distant controller over the IRIDIUM

satellite network or Freewave Spread Spectrum radio. Once the trigger event data has been

uploaded, the glider continues its mission, unless the controller remotely re-tasks the glider by

downloading new instructions.

1 600-second recordings were intended, but data files were shorter due to a logic error.

2 If required, this gap could be reduced or eliminated.

5

The operational life of an electric glider is one month depending on the energy demands of the

sensor suite. Between deployments, the glider's batteries can be replaced if needed.

2.2 Trial data

Data from five day trips were received and copied to a common hard drive, used for data

processing and analysis. A trial directory structure, consistent with the STAR analysis process

(Hood, J. 2011), was created for each deployment, while information and processing scripts

common to the trial were stored on the main directory. These are located in the trials09 STAR

repository under the directory name dnd_cetacean_feb2012.

A summary of the trial data collected each day is provided in Table 1. Sonobuoy recordings

include a short period of time at the start and end of recording where the sonobuoys were not

deployed and active:

Table 1: Summary of data collected during Glider 2012

Date STAR

trial name

Data collected for each sensor system

AN/SSQ-53F PARB SHARP (x 2 channels)

22 Feb 2012 sono_22feb 2.33 hrs x 2 sonobuoys none none

23 Feb 2012 sono_PARB_23feb 1.9 hrs x 2 sonobuoys 1.5 hrs (LP3) none

24 Feb 2012 PARB_SHARP_24feb none 1.5 hrs (LP3) 16.5 hrs (20-24 Feb)

27 Feb 2012 sono_PARB_27feb 2.07 hrs x 2 sonobuoys 3.0 hrs (LP4) none

29 Feb 2012 sono_PARB_SHARP_29feb 2.87 hrs x 2 sonobuoys 3.0 hrs (LP4) 16.5 hrs (27-29 Feb)

6

3 Data processing

This section provides detail on how trial data were processed by Akoostix. This processing was

performed using scripts that were executed via semi-automated processes. All data and scripts

related to this processing are located in the trials09 STAR repository under the directory name

dnd_cetacean_feb2012. Additional detail related to the algorithms and specific processing

settings can be found in this directory.

Raw acoustic data were received by Akoostix on a hard drive, provided after each day-trip. The

data were copied onto a common hard drive and processed as follows:

The data were converted into little-endian DREA-DAT-formatted files that could be

processed using SPPACS applications. This included addition of timestamp information.

This process is described in Section 3.1.

The acoustic data were processed to compute approximately 1.0 Hz spectra and 3rd

-octave-

averaged spectra for ambient-noise analysis. This process is described in Section 3.2.

The acoustic data were processed to automatically detect transients and cetacean

vocalizations. These results were stored in data files and logs compatible with STAR

detection analysis requirements, so that the results could be analyzed using STAR-IDL,

Omni-passive Display (OPD) and the Acoustic Cetacean Detection Capability (ACDC).

This process is described in Section 3.3.

3.1 Data formatting

Raw acoustic data from each sensor system was converted into little-endian DREA-DAT-

formatted files, timestamps were applied (for WAV source data), and the new files were stored in

the raw_data directory associated to each deployment. A subdirectory was created for each

sensor system (recorder), named to simplify identification of the source of the data and to allow

for use of sensor position information at a later date.

Acoustic data from each sensor type required different data formatting:

EADAQ data are recorded in time-stamped big-endian DREA-DAT-formatted files, so

sonobuoy recordings were byte-swapped to convert them to little-endean format using the

SPPACS application sp_byteswap.

PARB data were recorded in WAV files with time information stored in an accompanying

header file. Data were converted to DREA DAT format and time-stamped using the

SPPACS application sp_wav2dat.

Note: Data from 24 Feb did not have valid times in the header files, so data were

time-stamped using the time from the first GPS NMEA message, corresponding to

each file.

SHARP data were recorded as WAV files and were converted into DRDC DAT format

using the SPPACS application sp_wav2dat. Timestamps were either extracted a captured

directory listing, taken on the originating file system using the Python script sharp_times.py

7

and then patched using the SPPACS application sp_ph, or data were copied preserving the

file system times using the Linux cp command’s –p option.

All data formatting scripts can be found in the scripts subdirectory corresponding to each day-

trip, as documented in Section 2.2.

3.2 Ambient-noise processing

Acoustic data were processed to form 5-minute averaged 1.0 Hz spectra and then 3rd

-octave-

averaged spectra. These data were not calibrated, though these corrections could be applied at a

later date. The data may be used to examine the time-dependence of ambient noise in the area and

differences in acoustic sensitivity for each sensor system.

The ambient-noise processing stream is depicted in Figure 1. Minor differences, due to sensor-

dependent processing, are noted below:

Spectral processing FFT window sizes were rounded up to next power of two.

Spectra were limited to bands of 10 to 20 000 Hz for SHARP data and 5 to 40 000 Hz for

sonobuoy and PARB data.

Averaging time was reduced to 292 seconds for SHARP data to make best use of the 592-

second file length.

Specific processing parameters are documented in the ambient_3rd_octave.py Python script

located in the main_scripts subdirectory.

Figure 1: Block diagram of the processing stream used to generate 5-minute 1.0 Hz

ambient-noise spectra and 3rd

-octave spectra.

3.3 Automated detection of transients and cetacean vocalizations

One of the primary objectives of Glider 2012 was to exercise the sensor platforms described in

Section 2.1 and determine what marine mammals were present in the trial area. DRDC’s

automated detection algorithms were used to process these data and generate a set of detection

data for manual detection performance analysis. This section documents the automated

processing used to generate ACDC-formatted detections for each of the sensors.

8

The general approach applied to process data for each day is as follows:

DRDC’s automated detector was configured to detect a variety of cetaceans that might be

found near Halifax in winter. Further information on potential species and their

vocalizations is provided in Annex A, while the rationale and specific settings used to

configure the detector for each vocalization type is contained in the following sections. The

following detector configurations were created :

General transients

Sperm whales

Fin whales

Humpback whales

Minke whales

Sei whales

General squeals

Delphinids

Each detector configuration was applied to these data and detection logs were saved to the

NAD/transient_detect directory related to each data set. This processing was executed using

the Python script mammal_script.py, stored in the main_scripts directory, which used the

target files contained in the main_target_files directory.

These detection data were further processed to create additional logs and output data, which

are required for analysis in ACDC, using the post_extract.pro script located in the

main_idlprog subdirectory, as follows:

Cetacean detections were converted to annotations, and then clustered using the dive

event algorithm developed during the cetacean data and algorithm development

project (Bougher et al. 2012). The algorithm performed basic clustering, in which a

minimum detection spacing of 30 seconds was used to group detections. The

algorithm is capable more advanced filtering, but would require more advanced

settings for each detection target. This allowed for more efficient analysis, as a set of

detections could be analyzed as a set. Individual detection annotations and grouped

detection annotations were saved in the annotations and grouped_annotations

subdirectory of each deployment.

Grouped detections were extracted to wav and dat files using the times defined by

the grouped annotations plus 5 seconds of context at the beginning and end of each

file.

Detection logs were generated from the grouped annotations and were saved in the

acdc_input subdirectory of each deployment. The grouped detections logs allow

ACDC to associate annotations to detections when they are read into ACDC.

The pwr, ali, and eti data files, required by ACDC, were generated from the

extracted dat files using the SPPACS application sp_transient_post, and are saved in

the acdc_input subdirectory of each deployment.

9

Figure 2: DRDC’s automated transient detection stream. Refer to the online SPPACS help

for each application in bold to determine the available options for each program.

3.3.1 Transient

The detection stream for full-band transient detection was configured using the settings required

by the related contract’s statement of work (SOW). Band limits were adjusted to match the usable

band for each sensor type. Detection-configuration settings are defined in Table 2, and are also

contained in the transient*.tgt files located in the main_target_files directory. Two-speed

exponential averaging was used for background estimation, and exponential averaging was used

for signal estimation.

Table 2: Transient-detection settings

Recorder Time

Resolution [s]

Spectra Resolution

[Hz]

Band limits [Hz]

Normalization Guard band

Long noise integration

time [s]

Short noise integration

time [s]

Signal integration

time [s]

Detection threshold

[dB]

Sonobuoy 0.01 50 10:38000 None None 600 10 0.02 3.0 PARB 0.01 50 10:38000 None None 600 10 0.02 3.0

SHARPS 0.01 50 10:20000 None None 600 10 0.02 3.0

3.3.2 Sperm whale

The sperm-whale detector configuration was based on a previous configuration (Hood et al.

2008), though changes were applied to use the newer gapped-split-window detector option. The

gapped split window was configured to contain the duration of the click in the signal window,

ignore reverberation effects using the gap, and get a local estimate of the background noise

without including neighbouring clicks in the background window. Normalization and guard bands

were not used, as the gapped split-window provides sufficient normalization and loud sperm

whale clicks can large bandwidths, which prevent appropriate guard band selection. The same

configuration was used for all sensors, which is defined in Table 3 and contained in the

spermwhale.tgt file located in the main_target_files directory.

Table 3: Sperm-whale-detector settings

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]


Total window size [s]

Gap size [s]

Signal window size [s]

Detection threshold

[dB]

0.005 100 1000:5000 None None 0.5 0.1 0.005 3.0

10

3.3.3 Fin Whale

A fin-whale detector was configured to find the tonal frequency-modulated (FM) call described in

Annex A. Spectral normalization was not applied because the low frequency of the call would not

provide enough spectral content for background estimation. The spectral resolutions were

selected by considering the bandwidth of the call and the processing power required for larger

FFT as the sampling rate of the sensors is very high. The same configuration was used for all

sensors, which is defined in Table 4 and contained in the fin_whale.tgt file located in the

main_target_files directory.

Table 4: Fin-whale-detector settings

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]


limits [Hz]


Gap size [s]


Detection threshold

[dB]

0.1 10 15:28 None 100:200 15 03 1 3.0

3.3.4 Humpback whale

The humpback-whale-detector configuration is based on the configuration used in (Hood et al.

2008), with adjustments to benefit from spectral normalization and the gapped-split-window

detector. Humpback whales calls are relatively narrow bandwidth, so normalization enhances the

signals. The same configuration was used for all sensors, which is defined in Table 5 and

contained in the humpback_whale.tgt file located in the main_target_files directory.

Table 5: Humpback-whale-detector settings

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]


limits [Hz]


Gap size [s]


Detection threshold

[dB]

0.1 5 200:500

625:1550 500 Hz Median

600:1000 200:500

50 10 1 3.0

3.3.5 Minke whale

The minke-whale detector was configured to using information on Minke whale clicks provided

in Annex A and analysis sound samples from Mobysound (Mellinger and Clarke 2006). The same


minke_whale.tgt file located in the main_target_files directory. Another option that was

considered for the band limits was 100 Hz to 400 Hz.

Table 6: Minke-whale-detector settings

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]

Normalization Guard

band limits [Hz]

Total window size

[s]

Gap size [s]


Detection threshold

[dB]

0.02 25 100:600 None 800-1200

0.5 0.1 0.04 3.0

11

3.3.6 Sei whale

The sei-whale detector was based on the recommendations documented in Annex A. The same


sei_whale.tgt file located in the main_target_files directory.

Table 7: Sei-whale-detector settings

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]



Gap size [s] Signal

window size [s]

Detection threshold

[dB]

0.02 25 100:400 None 800-1200

0.5 0.1 0.04 3.0

3.3.7 General squeals

One detector was configured to find non-species-specific squeals. Northern bottle nose whale

squeals were used as an example when selecting the detector settings. Spectra were normalized

using the median option and ETI were generated using overlapping bands. The configuration is

described in Table 8 and in the general_squeal.tgt file located in the main_target_files directory.

Table 8: Detector settings for squeals

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]



Gap size [s]


Detection threshold

[dB]

0.15 23.43

5000:10000 8000:13000

11000:16000 14000:19000

2500 Hz Median

None 1.0 0.5 0.1 3.0

12

3.3.8 Delphinids

The delphinid detector was configured for high-frequency clicks as recommended in Annex A.

These clicks can occur at a variety of frequencies and bandwidths, so normalization and guard

band options were not considered beneficial. Since the configuration is intended for non-specific

clicks, multiple overlapping bands were used. Due to bandwidth limitations, the SHARP recorder

could not use all detection bands. The gapped split window was configured to limit the noise

estimate to the region between clicks. The configuration is described in Table 9 and in the

delphinids*.tgt files located in the main_target_files directory.

Table 9: Detector settings for delphinid clicks

Time Resolution

[s]

Spectra Resolution

[Hz]

Band limits [Hz]



Gap size [s]


Detection threshold

[dB]

0.002 187.5 6000:1500

10000:20000 25000:38000*

None None 0.05 0.01 0.002 4.0

*Not implemented for the SHARP recorder

13

4 Data analysis

This section provides detail related to the trial data analysis task, which was performed on data

processed as described in Section 3. Section 4.1 describes the results of ambient-noise processing,

while automated detection results are provided in Section 4.2, where detector performance was

also verified using manual analysis. All data, notes, and logs related to this analysis are located in

the trials09 STAR repository under the directory name dnd_cetacean_feb2012. Additional detail

related to this analysis and a complete set of results can be found in this directory.

4.1 Ambient analysis

All acoustic recordings were processed to determine noise levels using the approach documented

in Section 3.2. These processed results were further analyzed using the STAR-IDL script

process_AN_data2.pro, which is stored in the main_idlprog directory for the trial. This script

provides two general processing modes:

Statistic generation: In this mode, the minimum, maximum, median, and average spectral

and third-octave level for each frequency bin is computed independently for each sensor and

data set. These results are stored in CSV files and plots are captured in EPS files, stored in

the analysis_results subdirectory related to each data set. A sample of one of these plots is

shown in Figure 3 and Figure 4, where it is apparent that levels for lower frequencies (10 Hz

to 30 Hz) were most dynamic over this period.

Movie mode: In this mode, each 5-minute averaged spectra is plotted in sequence, indicating

the time of the recording in the title. Analysts can then view changing spectra versus time at

full dynamic range. These plots can be captured using commercial-off-the-shelf (COTS)

tools (e.g. Screenflow for OSX) for more detailed analysis.

This analysis reinforced the highly dynamic nature of acoustic sound levels, showing different

weather conditions and passing ships. Further options for visualizing and analyzing these data are

considered in Section 6.3.5.

14

Figure 3: Plot of statistical analysis of spectral levels for approximately two hours of data

from one AN/SSQ-53F deployed on 23 Feb 2012 (15:01Z to 16:55Z). The strong

signals between 500 Hz and 1000 Hz are likely due to the trial vessel.

Figure 4: Plot of statistical analysis of third-octave average levels for approximately two hours

of data from one AN/SSQ-53F deployed on 23 Feb 2012 (15:01Z to 16:55Z). The strong

signals between 500 Hz and 1000 Hz are less prominent than for Figure 3.

15

4.2 Detection analysis

Automated detection for marine mammals is an evolving monitoring technique that currently

requires some degree of vetting by manual analysis to improve the quality of the result. This

section discusses the manual analysis process, automated detector performance, and the overall

detection performance including effort required to inspect and confirm automated detections.

At a high level, the following processing was used for analysis of the automated detections:

Detection density plots were created to summarize all detections that occurred for a specific

PAM sensor data set on a single page.

Some data were analyzed in detail using this process:

All grouped detections were loaded into ACDC and carefully examined, both

visually and aurally.

Each detection grouping was classified as target, possible, or non-target using

ACDC.

Where classification was not possible in ACDC, associated WAV files were

examined in Audacity.

Non-biological signals that caused, or were likely to cause, detection events were

annotated.

Other data were analyzed using an abbreviated process:

All grouped detections were loaded into ACDC and quickly examined.

Each detection grouping was classified as target, possible, or non-target using

ACDC.

Where classification was not possible in ACDC, associated WAV files were

examined in Audacity.

Detection density plots (Figure 5) were produced to summarize the results of the automated

detections for each set of recordings obtained for each PAM system. These plots were examined

to determine likely non-target detections (false alarms) based on expected vocalization behaviour

of the target animals and sections of recordings likely associated with noise events. For example,

sperm whales produce long echolocation click trains, emitting clicks at a rate of approximately

1-2 clicks per second or 60-120 clicks/minute (Whitehead 2003). They are not known for

producing single clicks and therefore a single detection is likely a false alarm. Seventeen such

single click detections (based on a rate of at least one per minute) could be classified as non-

sperm-whale (Figure 5). As well, a sudden increase in detection rates on most or all detectors

indicate likely noise events. For example, detection rates for most species increased during the

last 10 minutes of the sonobuoy recordings when the RF signal began fading in and out (see

Figure 5). On average, approximately 50% of the false detections examined could be classified as

non-target signals from the density plots alone (though it is important to note that this percentage

varied with the type of detector and the type of sensor (Table 10 through Table 12).

16

Figure 5: Example of a Detection Summary Plot showing when detections occurred for each

detector type. Green bars indicate periods for which recordings were obtained, red bars would

indicate periods where no recordings were obtained (not present in this case). Each

triangle marks the number of detections obtained within a one-minute period.

The total number of detections obtained from each detector is given in

parenthesis to the right of the detector configuration name.

17

Table 10: Identification of false detections from Detection Summary Plots,

example shown for sonobuoy recordings from 22 Feb 2012.

Detector Total number of detections

Estimated number of false detections identified from Detection Summary Plot

Proportion of false detections identified from Detection Summary Plot

fin whale 1 1 1.00

sei whale 7 3 0.43

humpback whale 8 0 0.00

minke whale 56 39 0.70

general squeal 9 9 1.00

sperm whale 650 336 0.52

delphinids 7 910 3 313 0.42

All 8 689 3 701 0.43


example shown for PARB recordings from 23 Feb 2012.

Detector Total

number of detections



fin whale 1 1 1.00

sei whale 7 3 0.43




sperm whale 6 159 1 740 0.28

delphinids 2 977 1 821 0.61

All 9 677 3 605 0.37

Detections were manually inspected to evaluate detector performance by determining if the

detections were target cetacean vocalizations or other non-target signals (such as system or

anthropogenic noise). Due to time constraints, a manual analysis of the detections could not be

completed for all of the recordings. Table 13 summarizes the recordings that were manually

analyzed.

18


example shown for SHARP recordings from 24 Feb 2012.

Detector Total

number of detections1



fin whale 19 13 0.68

sei whale 7 4 0.57




sperm whale 3 435 10 < 0.01

delphinids 353 8 0.02

All 5 434 46 0.01

1Data examined from the first four hours of the SHARP recordings are presented in this Table.

Table 13: Status of manual analysis

Date of recording

Recording system Total minutes of recording

Status of manual analysis

02/22/2012 to 02/24/2012

SHARP (2 channels) 1980 Partially complete

02/22/2012 Sonobuoys (2 channels) 280 Complete

02/23/2012 Sonobuoys (2 channels) 230 Complete

02/23/2012 PARB 90 Complete

02/24/2012 PARB 90 Partially complete

02/27/2012 to 02/29/2012

SHARP (2 channels) 1980 Not analyzed

02/27/2012 Sonobuoys (2 channels) 248 Not analyzed

02/27/2012 PARB 181 Not analyzed

02/29/2012 Sonobuoys (2 channels) 348 Not analyzed

02/27/2012 PARB 181 Not analyzed

19

Manual analysis of the detections was completed primarily using the Acoustic Cetacean

Detection Capability (ACDC) software version 2.1. Here sets of detections, clustered into groups

using the approach described in 3.3, were examined as one. This increased analysis efficiency as

similar signals usually occurred in sets. Spectrograms for each set of grouped detections were

visually scanned and most of the grouped detections were also aurally scanned. Audacity (v 1.3.8)

was used to further examine the grouped annotations, if a more detailed analysis was required

such as zooming in on the time or frequency axes of the spectrogram, playing back a particular

time segment, increasing the amplitude of the recording or filtering out specific frequency bands.

It should be noted that manual analysis of the detections would become more efficient if ACDC

were able to perform some additional functions such as those mentioned above (see

Section 6.3.3).

Each of the grouped annotations were classified as either target (cetacean signals produced by the

species of interest), possible (signals possibly produced by cetacean species of interest), or

non-target (false alarms; signals not produced by the cetacean species of interest). Additionally,

noise sources and causes of false alarms were annotated for the first set of recordings examined

for a particular recording system to gain a better understanding of sources of false alarms for each

type of detector.

In total, 2 005 grouped annotations, which included 81 165 individual detections, were manually

analyzed. These included 737 grouped annotations (16 360 individual detections) from the

sonobuoys, 296 grouped annotations (31 659 individual detections) from the PARBs, and 972

grouped annotations (33 146 individual detections) from the SHARPs. Sperm whale and

delphinid click detections account for the majority (~70%) of these detections (see Table 14,

Table 15, and Table 16). Recordings from 22, 23, and 24 Feb 2012 were analyzed for this part of

the effort. The full results of the manual analysis were delivered to DRDC as an MS Excel file

that is archived with the STAR-formatted trial data, logs, and scripts.

No cetacean vocalizations were detected on any of the days from which recordings were

analyzed. There were some (15 grouped annotations) high-frequency clicks that were possibly

produced by delphinids (see Table 17 and

Table 18), however, these were generally only one or a couple of clicks produced with irregular

timing that are more likely to be random system noise. All of the detections analyzed were thus

likely false alarms, or would at least be rejected by most advanced analysis processes. It is

important to note that despite the fact that all of the detections were false alarms, this automated

detection approach significantly decreases the amount of time required to determine the presence

(or absence) of cetacean vocalizations on the recordings. Fully analyzing acoustic recordings

manually for the presence of cetaceans requires a considerable amount of time as all of the

acoustic data collected must be visually and aurally inspected. Furthermore, in order to determine

the presence of both baleen whale and toothed whale vocalizations, the acoustic data must be

visually and aurally inspected twice; once at full bandwidth to determine the presence of higher

frequency vocalizations and once with a low-pass frequency filter applied to determine the

presence of the very low frequency baleen whale calls. The time expected to complete a full

manual analysis of acoustic data for the presence of cetacean vocalizations is thus generally

expected to take at least twice the duration of the recording itself (and in most cases much longer

than this). Even with high false alarm rates, automated analysis reduces the amount of manual

analysis effort required to determine the presence of cetacean vocalizations. For example, during

20

this study 510 minutes of sonobuoy recordings were analyzed in approximate 330 minutes. The

time needed to analyze the recording is even quicker when an abbreviated analysis process is used

– 90 minutes of PARB recordings took 149 minutes to analyze in detail (including the time taken

to annotate obvious noise sources); but when just classifying the grouped annotations, 90 minutes

of PARB recording only took 48 minutes to analyze.

Although the types of signals that the detectors were detecting are similar to the target

vocalizations they should be detecting, detector performance could not be accurately assessed

because no target signals were actually present on the recordings. The detection threshold settings

were relatively low to ensure that if cetacean vocalizations were present they would be detected.

This also means that background noise can frequently trigger detections. The number of false

detections could be reduced by increasing the threshold settings on the detectors. For example,

the initial delphinid detector had a detection threshold value of 4.0 (Table 9). With this setting,

7 910 false delphinid detections occurred on the sonobuoy recordings from 22 Feb. When the

detector threshold value was increased to 8.0, the number of false delphinid detections decreased

to 1 462 (or 18% of the original number of false detections). Similarly, other detector settings

may be adjusted to decrease the number of false alarms. The minke-whale detector used a

frequency band of 100-600 Hz (Table 6), which resulted in 254 false detections on the 23 Feb

sonobuoy recording; many of which were caused by ship engine noise in the 500-600 Hz

frequency range. By changing the band limits to 100-400 Hz, the number of false minke-whale

detections was reduced to 166 (65% of the original number of false detections). However;

because no cetacean signals were present on the recordings, it is not known if increasing detection

thresholds would result in not detecting cetacean signals that are present. Testing the detectors on

an acoustic dataset which has cetacean vocalizations present would allow detector settings to be

adjusted to maximize the number of present cetacean signals detected while minimizing the false

alarm rate. Additional analysis would also help to conceive innovative improvements and post-

processing options for cetacean detectors (see Section 6.3.1 and Section 6.3.2).

General observations about the causes of false detections on each of the systems were made.

Based on the detailed analysis of the 22 Feb sonobuoy data, 23 Feb PARB data, and 24 Feb

SHARP data, the detection rate varied with species and system used, ranging from 0.004 fin-

whale detections/min for the sonobuoys to 110 delphinid detections/min for the PARBs

(Table 14, Table 15, and Table 16). The causes of these detections are given in (Table 17,

Table 18, and Table 19). Fin whale, sperm whale, and delphinid false detections were generally

caused by system noise such as knocking, clanging, clicking, and inconsistencies in the RF signal.

Humpback whale and squeal false detections were caused mostly by ship engine noise, while sei

whale and minke whale false detections were frequently caused by both system noise and ship

noise, as well as by non-obvious changes in the background noise within the low frequency

bands. Additional effort should be applied to improve false-alarm rejection, especially for click-

like signals. Some research related to this problem was conducted during a parallel effort

(Bougher et al. 2012; Theriault et al. 2011), but should continue.

The greatest issue with the sonobuoys was system noise, including the RF signal fading in and out (

Figure 6). Many of the false alarms could be eliminated by omitting acoustic data collected when

the sonobuoy was first deployed, causing excessive noise as the hydrophone deploys, and at the

end of the recording when the signal starts to fade, causing excessive RF noise.

21

Loud ship engine noise was a problem for all three of the systems (

Figure 6). Omitting portions of recordings likely to have loud ship engine noise present (such as

at the beginning of recordings when systems are first deployed and the end of recordings when

systems are recovered) would eliminate many false alarms, as well as shutting off engines or

moving away from the acoustic recorders while acoustic monitoring is in progress.

The SHARP system also had many baleen whale detections triggered by what appear to obvious changes in the background noise within the low frequency bands (

Figure 6), suggesting that these systems may not be ideal for detection of the lower frequency

baleen whale calls. Relatively fewer delphinid detections occurred on the SHARP recordings as

compared to the sonobuoy and PARB recordings, (which were dominated by delphinid

detections); likely because the SHARPs only recorded up to ~22 kHz while the sonobuoys and

PARBs recorded up to ~40 kHz.

The manual effort required to determine the validity of the detections that did occur was assessed

based on detailed analysis of the 22 Feb sonobuoy data, 23 Feb PARB data and 24 Feb SHARP

data (Table 13). The highest detection rates generally occurred for clicks (the sperm whale and

delphinid detectors), thus these were typically the fastest detections to analyze as many detections

could be assessed at once by using the grouped annotation. The low-frequency baleen whale

pulsed calls or frequency upward sweeps (the fin-whale, sei-whale and minke-whale detectors)

took the longest time to analyze because they often required opening the recording in Audacity to

filter out higher frequencies so that the low frequency band could be heard. In terms of the

average number of detections analyzed per minute, analysis was fastest for the PARB data and

slowest for the sonobuoy data (Table 14 and Table 15).

Table 14: Manual analysis effort where the detections were analyzed in detail,


Detector Total


Detection rate

(detections /min)

Number grouped

detections

Average time to analyze (number of grouped detections

/min)

fin whale 1 0.004 1 0.25

sei whale 7 0.025 2 1.00

humpback whale 8 0.029 3 0.33

minke whale 56 0.200 47 0.43

general squeal 9 0.032 3 0.33

sperm whale 650 2.321 125 0.20

delphinids 7 910 28.250 155 0.58

transient 48 0.171 4 0.50

All 8 689 31.032 340 0.42

22



Detector Total


Detection rate

(detections /min)

Number grouped

detections

Average time to analyze (number

of grouped detections /min)

fin whale 1 0.011 1 1.00

sei whale 7 0.078 6 0.17


minke whale 418 4.644 41 1.71

general squeal 58 0.644 3 0.33

sperm whale 6 159 68.433 4 4.50

delphinids 2 977 33.078 11 1.55

transient 52 0.578 6 0.67

All 9 677 107.522 73 1.54


example shown for SHARP recordings from 24 Feb 2012.

Detector Total


Detection rate

(detections /min)

Number grouped

detections analyzed1

Average time to analyze (number

of grouped detections /min)

SHARP recordings from 02/24/2012

fin whale 1 814 1.832 352 20.16

sei whale 9 509 9.605 100 792.42


minke whale 4 377 4.421 68 273.56

general squeal 1 857 1.876 31 132.64

sperm whale 13 392 13.527 65 787.76

delphinids2 2 140 2.162 Not analyzed Not analyzed

transient 21 472 21.689 57 1 651.69

All 33 146 33.481 644 204.60 1Due to time constraints, only a portion of the grouped annotations could be analyzed; therefore this number represents only the grouped annotations which were analyzed and not the complete set of the grouped annotations for the recording.

2Due to time constraints, analysis of the delphinid annotations could not be completed and thus delphinid results were not included in this analysis.

23



Detector Number of grouped

annotations analyzed

Number of target

Number of

possible targets

Number of non-targets

Cause of false alarms (% of grouped annotations caused

given in brackets)

fin whale 1 0 0 1 RF signal (100%) sei whale 2 0 0 2 RF signal (50%)

Unknown causes, static (50%) humpback whale 3 0 0 3 Ship engine noise (100%)

minke whale 47 0 0 47 RF signal (18%) Other system noise (12%) Ship engine noise (6%) Unknown causes, static (64%)

general squeal 3 0 0 3 RF signal (100%) sperm whale 125 0 0 125 RF signal (5%)

Other system noise (79%) Ship engine noise (14%) Unknown causes, static (2%)

delphinids 155 0 12 145 RF signal (95%) Ship engine noise (5%)

transient 4 0 0 4 RF signal (100%)





Number of

target

Number of

possible targets



given in brackets)

fin whale 1 0 0 1 System noise (100%) sei whale 6 0 0 6 System noise (33%)

Ship engine noise (66%) humpback whale 1 0 0 1 Ship engine noise (100%) minke whale 41 0 0 41 Ship engine noise (80%)

Unknown causes, static (20%) general squeal 3 0 0 3 Ship engine noise (100%) sperm whale 4 0 0 4 System noise (100%) delphinids 11 0 3 10 System noise (90%)

Depth sounder (10%) transient 6 0 0 6 System noise (17%)

Ship engine noise (66%) Depth sounder (17%)

24


example shown for SHARP recordings from 24 Feb 2012



Number of

target

Number of

possible targets



given in brackets)

fin whale 352 0 0 42 System noise (<0.01%) Ship engine noise (18%) Unknown causes, static (82%)

sei whale1 100 0 0 100 System noise Ship engine noise Unknown causes, static

humpback whale 28 0 0 28 System noise (64%) Ship engine noise (29%) Unknown causes, static (7%)

minke whale 68 0 0 68 System noise (6%) Ship engine noise (46%) Unknown causes, static (48%)

general squeal 31 0 0 28 Ship engine noise (78%) Depth sounder (6%) Unknown causes, static (6%)

sperm whale 65 0 0 65 System noise (3%) Ship engine noise (12%) Unknown causes, static (85%)

delphinids2 Not analyzed

transient 57 0 0 57 System noise (3%) Ship engine noise (40%) Depth sounder (3%) Unknown causes, static (54%)

1Due to time constraints, assessment of the cause of the sei whale false detections could not be completed and thus these data are not presented here. 2Due to time constraints, analysis of the delphinid annotations could not be completed and thus delphinid results were not included in this analysis.

25

Figure 6: Causes of false alarms determined from detailed manual analysis of three sets

of recordings (examples from each of the three recordings systems used).

Automated detection of cetacean vocalizations is still a relatively new technology that is

improving as more effective recording systems, better algorithms, and faster computers are

developed. The results presented here provide an initial indication of the benefits and limitations

of the automated detection algorithms, however, an acoustic dataset with cetacean vocalizations

present are required to accurately assess detector performance and determine how to best adjust

detector settings to maximize the probability of detecting vocalizations of the various species

which minimizing the occurrence of false alarms.

26

5 Passive acoustic monitoring (PAM) options

More than 20 species of cetaceans have been sighted in Atlantic Canadian waters, 15 of which are

known to occur regularly on the Scotian Shelf. While some of these species are resident in the

area year-round, many species are migratory and occur only seasonally (Breeze et al. 2002). The

distribution and abundance of most of these species within the region have not been described in

detail and as a result, no studies provide a comprehensive review of cetacean distribution and

abundance on the Scotian Shelf, though Breeze et al. (2002) provides a broad overview. Some

data (such as historical sightings data) are available to help assess which areas of the Scotian

Shelf are frequently used by cetaceans, but conclusions that can be drawn from these data are

limited because of temporal and spatial gaps in coverage. PAM offers a means of gaining

information about the presence and abundance of cetaceans over a broad range of spatial and

temporal scales.

Three technologies which show promise for acoustic monitoring of marine mammals (especially

cetaceans) in MARLANT Operational Areas (MARLOA) are investigated in this report. Effective

acoustic monitoring of marine mammals will allow naval units to detect marine mammals within

an area before, during and post exercises, and ultimately may help identify areas and times when

marine mammal encounters are least likely, thus allowing for more precise selection of naval

exercise areas and time periods to mitigate for the presence of marine mammals.

The following sections review three different types of acoustic monitoring technologies (with

examples of specific sensor systems for each type) and the operational issues that are involved

with operating each. The biological considerations that need to be addressed when choosing a

technology for the purpose of marine-mammal acoustic monitoring are also discussed, with

specific reference to the advantages and disadvantages of the three types of technologies reviewed

in this document.

5.1 Technologies reviewed

The three technologies reviewed in this document are real-time floating analog sensors, acoustic

sensors onboard autonomous underwater vehicles, and bottom-moored acoustic systems. A

description of these technologies is provided herein with specific detail for selected systems in

Section 2.1.

5.1.1 Real-time floating analog sensors

Real-time floating analogue sensors, also referred to as sonobuoys, are relatively small,

expendable passive acoustic sensors. These free-floating sensors consist of a suspended

hydrophone tethered to a surface float equipped with a VHF-FM radio transmitter that transmits

analogue acoustic signals to receivers within line of sight (potentially 100 nm for high altitude

aircraft). These systems are easily deployed from ships and specially-equipped aircraft with up to

an 8-hour operating life. A specific example of this type of sensor is the AN/SSQ-53F sonobuoy,

which is described in more detail in Section 2.1.1.

27

5.1.2 Acoustic sensors onboard autonomous underwater vehicles

Autonomous underwater vehicles (AUV) are a type of unmanned underwater vehicle that can

travel without real-time input from an operator, though some may be controlled remotely.

Although individual AUV designs have mobility limitations, all can be programmed to travel

specific routes, directions, speeds, and depths. AUVs may be equipped with passive acoustic

sensors to collect data, which may also be processed onboard.

The Slocum Gliders operated by DRDC include an acoustic payload and are an example of this

type of system. These are variable-buoyancy AUVs that patrol an area by navigating between pre-

programmed waypoints, varying depth to generate motion. These are ship-deployable systems

that must be retrieved. After deployment, the glider surfaces at selected times, which can be used

to communicate with the glider in order to change the mission, adjust the configuration, or upload

data via an Iridium satellite link. Data transfer via Iridium is bandwidth constrained so lengthy

full-bandwidth acoustic data files cannot be transferred remotely; instead processing logs and

short data chunks may be downloaded or full recordings are obtained once the glider is recovered.

The acoustic payload within the DRDC gliders consists of a hydrophone with a frequency

response of 10 Hz to 100 kHz, though current electronics is limited to 40 kHz. The acoustic

subsystem (AS) records data to 16-bit WAV-formatted data files at a user-configured sampling

rate, up to 100 kHz. Acoustic data is sampled on a duty-cycle, governed in part by when the

glider is able to restrict use of motors (such as only during quieter descent or ascent modes), as

motor noise corrupts acoustic recordings . The operational life of a glider equipped with the AS is

approximately one month with alkaline batteries.

5.1.3 Bottom-moored acoustic systems

Bottom-moored acoustic systems are autonomous recorders equipped with hydrophones and

anchored to the seafloor at specific depths above the seabed. They are deployed from ships at a

predetermined location and can collect acoustic data over relatively long time periods. The

systems are generally recovered by acoustically activating a release mechanism that allows the

system to disconnect from the anchor, although some systems may be pre-programmed to release

at a specific date and time. The acoustic sensors themselves are positively buoyant, or are

equipped with buoys or floats, so once released from the anchor they will float to the surface

where they can be recovered. Acoustic data cannot be transferred remotely and therefore can only

be obtained upon recovery of the system.

One example of a bottom-moored acoustic recorder is the DRDC Atlantic designed and built,

SHARP. The recording capabilities of SHARPs are described in more detail in Section 2.1.2.

Jasco Research (www.jasco.com) developed and provides the Autonomous Multi-Channel

Acoustic Recorders (AMAR) bottom-moored acoustic recorder. AMARs are equipped with

GeoSpectrum M8 hydrophones that are able to record frequencies up to 150 kHz. Their

multichannel capabilities allow them to record acoustic data at different sampling rates,

resolutions, and duty cycles on different channels, thus the recording capabilities of the system

can be customized for the user. The upper limit of the sampling rate for the systems is 384 kHz.

The operational life varies depending on the sampling rate and duty-cycle used, but is typically

constrained by the storage capacity of the system. AMAR can be equipped with 1.0-1.5 TB of

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storage and can generally record for months to a year at a time. These systems can be deployed to

depths of up to 2500 meters.

5.2 Operational issues

PAM technologies cannot be used in isolation as their deployment can affect other operations,

require specific capability, or limit the utility of resulting data. The following subsections discuss

operational issues that must be considered when selecting a PAM technology, while providing

specific examples for the systems that were reviewed. Much of the information presented in these

sections was developed using information from the MARLANT Marine Mammal Mitigation

Procedures (Department of National Defence In Progress), the MARLANT Operating Area

Management Plan (LGL Ltd. Environmental Consultants 2005), and was also derived from

discussions with navy personnel (Penney, pers. comm. 2012, Truscott, pers. comm. 2012). The

operational considerations discussed below are summarized in Table 20.

Table 20: Operational considerations related to the four PAM technologies that were reviewed.

Operational issue

Type of system

Floating analogue AUV-based Bottom-moored

AN/SSQ-53F sonobuoy Slocum Glider SHARP/AMAR

Duration of monitoring

Up to 8 hours Up to 1 month Several days (SHARP) Up to 1 year (AMAR)

Timing of monitoring

Short term monitoring only (before, during and post-

exercise)

Short term monitoring (pre- and post-exercise)

Short term monitoring (pre- and post-exercise) or long-term

monitoring

Acoustic security

Not likely to be an issue Possibly an issue with longer

term deployments Could be an issue

Deployment and recovery

Deployed by ship or aircraft, not limited by weather/sea

state, some training required

Deployment from ship or shore, limited by weather/sea

state, requires trained personnel

Deployment from ship, somewhat limited by

weather/sea state, requires trained personnel

Mutual interference

Not an issue

Likely an issue for towed sensors and submarines

(gliders should be recovered before exercise starts)

Could be an issue for towed sensors and submarines (dependant on operating

depths)

Approximate recording frequency

range3

10-40 kHz 10-40 kHz 10-40 kHz (SHARP) 10-150 kHz (AMAR)

3 These values are based on the current system specification. Actual values may vary due to changes in

individual components including hydrophone, suspension system, and electronics.

29

Operational issue

Type of system

Floating analogue AUV-based Bottom-moored

AN/SSQ-53F sonobuoy Slocum Glider SHARP/AMAR

Data transfer rate

Real-time data upload, but can be affected by RF issues (interference, antenna wash-

over in high sea-state)

Some data can be uploaded via INMARSAT, but not long recordings. Communication quality may be affected by antenna wash-over in high

sea-state, data downloaded after recovery (up to an hour)

Data downloaded after recovery

(up to an hour - SHARP) (up to a day – AMAR)

Analysis

First level analysis conducted during data collection (at

sea), subsequent analysis shore-based

Extensive shore-based analysis

Extensive shore-based analysis

Approx. cost/unit

$1,000 $150,000 $10,000 SHARP $50,000+ AMAR

Operational effectiveness

Effective for short-term monitoring and data

collection in active ASW exercise areas.

Effective for short- to medium-term / pre- and post-exercise

data collection. Cannot be used in active ASW exercise

areas.

Most effective for long-term data collection, when

significant lag in results is acceptable. Can be used in

active exercise areas if equipment is deeper than ASW sensors or deepest submarine

operating depth.

5.2.1 Acoustic monitoring locations

The MARLOAs consist of a large area of the western North Atlantic Ocean between

Newfoundland and the Bay of Fundy, including the Scotian Shelf, slope and offshore deep water

locations (see Figure 7). This area includes commercial fishing areas, commercial shipping lanes,

off-shore oil and gas infrastructure, and non-exercise naval transit areas. The potential impact of

these activities on acoustic equipment and data should be considered when planning PAM. For

example, potential damage to, or loss of, sensors should be considered when deploying AUVs in

high-traffic areas or bottom-moored sensors in known bottom-trawling grounds.

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Figure 7: MARLANT operational areas.

31

5.2.2 Duration of acoustic monitoring

The duration of acoustic monitoring varies with the sensor system, so selection of PAM

technology will depend on the intended application for the data. Sonobuoys have the shortest

recording lifespan of any of the sensors, with a life of eight hours or less. Gliders are able to

collect data over periods of weeks, while the bottom-mounted systems tend to have the longest

recording lifespan with an ability to collect data for many months.

5.2.3 Timing of acoustic monitoring

Two general types of acoustic monitoring for marine mammals are considered: short-term and

long-term monitoring.

Short-term PAM may occur prior to, during and after completion of anti-submarine warfare

(ASW) active-sonar exercises. The purpose of short-term acoustic monitoring is to determine the

presence of marine mammals within an area before an exercise begins (pre-exercise monitoring),

to detect the presence of marine mammals during an exercise, and/or to assess the potential

effects an exercise had on marine mammals (changes apparent between pre- and post-exercise

monitoring results). If pre-exercise monitoring is conducted as a mitigation measure for avoiding

marine mammals, it must be done in sufficient time to assign a new exercise area if sufficient

marine mammal activity is detected. Depending on the complexity of the exercise and number of

ASW units involved, three to five working days would be needed to reschedule a less complex

exercise, while one to two week notice would be required for a more complex exercise.

Consequently, PAM must be conducted two to four weeks prior to the start of an exercise to

provide sufficient opportunity for data analysis and decision making. Monitoring during an

exercise can be conducted as operationally convenient and/or as required for marine mammal

detection (as part of mandated mitigation measures). Post-exercise monitoring should be

completed in sufficient time to determine potential active-sonar impacts on the distribution and

abundance of marine mammals in the affected area, as directed by the Scientific Authority and/or

Formation Environment. While all of the sensor systems reviewed can be used for short-term

acoustic monitoring pre- and post-exercise, real-time acoustic sensors, such as sonobuoys, are

required for acoustic monitoring during trials.

Long-term acoustic monitoring is required to develop knowledge of which areas are important to

marine mammals and when they are used. Significant areas include feeding, breeding and calving

grounds, or migration routes that are used consistently by the animals over time. This information

is required to facilitate the selection of MARLOAs for ASW active-sonar operations with the aim

of decreasing the likelihood of conducting exercises where and when marine mammals are

present. Longer term monitoring for this sort of data collection can be conducted as resources

permit. Given the ability of bottom-moored systems to record for periods of weeks-months,

sensors such as AMARs and SHARPs are ideal for long-term monitoring studies.

5.2.4 Acoustic security

Naval ships and submarines deploy and transit routinely in the MARLOAs outside of planned

exercises so acoustic data collected in these areas may include sensitive data, such as active-sonar

transmission information, ship signatures, and submarine signatures, and therefore requires

32

vetting prior to release. PAM may create an acoustic security issue if naval ships and submarines

are unknowingly recorded. The potential for recording sensitive data increases with duration of

acoustic monitoring, and is of particular concern for autonomous acoustic sensors, as they may

not always be accompanied by a ship or aircraft. For this reason, sonobuoys, which only operate

for up to eight hours and are always accompanied by a nearby platform, are not likely to create an

issue with acoustic security. However, acoustic security is much more likely to be an issue with

bottom-moored acoustic recording systems such as SHARPs and AMARs, which can record

continuously for weeks to months at a time. AUV such as the Slocum Glider could also record

sensitive data without the knowledge of military platforms, but these are often accompanied by a

tender vessel, reducing the likelihood of security issues. Acoustic security may become a greater

issue for gliders as longer term deployments become more routine.

The Acoustic Data Analysis Centre Atlantic (ADAC(A)) is responsible for acoustic data analysis

and ensuring that all data is handled in line with the RCN’s acoustic security policy. Acoustic

data that may contain sensitive data should be vetted by ADAC(A) or ADAC(P) (Pacific) to

declassify recordings prior to release.

5.2.5 Deployment and retrieval of sensors

While all of the systems investigated are capable of being deployed by ship, sonobuoys may also

be deployed by fixed and rotary wing maritime aircraft that are fitted with special equipment to

deploy sonobuoys. Sonobuoys are routinely launched and monitored from RCN ships and

aircraft. Deployment of sonobuoys is not limited by weather conditions or sea-state, and

sonobuoys do not need to be recovered. Thus, in terms of deployment and retrieval requirements,

sonobuoys are relatively easy sensors to work with. However, although deployment of sonobuoys

does not require extensive training, trained personnel and special receivers are needed to collect

the acoustic data itself.

Slocum Gliders can be launched from ships or shore. Deployment and recovery at sea is easiest

from small boats (such as zodiacs and rigid hull inflatable boats [RHIB]), though they can be

deployed from the deck of larger ships. Many warships are suitable platforms for glider

deployment and retrieval. Gliders are relatively delicate systems and reasonably calm conditions

are needed for safe deployment and recovery, thus their use is highly restricted by weather

conditions and sea state. Without specialized training for ship’s crews, trained technical personnel

would need to be embarked to ensure the gliders are launched, operated, and recovered properly

and safely.

Bottom-mounted sensors can be deployed from vessels of various sizes, though ships equipped

with winches are ideal. Similar to gliders, many warships are suitable platforms for deployment

and recovery of SHARPs and AMARs. Deployment and retrieval of these sensors are also

dependant on weather conditions and sea-state, though not to the same extent as for gliders.

Training is required for safe deployment and retrieval of these systems.

5.2.6 Mutual interference

Mutual interference would occur if PAM systems interfere with ASW sensors or ASW exercise

participants. In this case, collision or entanglement risk is highest. Sonobuoys are designed to

33

avoid mutual interference with ASW sensors or submarines. Both Slocum Gliders and bottom-

moored acoustic recorders may cause mutual interference issues with towed arrays, variable depth

sonar (VDS), towed torpedo decoys, and submerged submarines. To mitigate mutual interference

between Gliders and ASW sensors and submarines, gliders must be recovered before any ASW

exercise area is activated. Depending on the depth of water, bottom-moored PAM systems can be

left deployed, but the shallowest component must be deeper than the lowest operating depth of

any towed sensor or submarines, including a safety separation buffer. The operating depth zones

of submarines and towed sensors are dependent on the aim of the exercise, depth of the water, and

environmental conditions and will be published as part of the exercise instructions by the exercise

planners and the submarine operating authority (SUBOPAUTH). Ideally, PAM plans should be

considered in mutual interference avoidance plans for any exercise.

5.2.7 Recording frequency range

It is important to consider the recording frequency range for PAM, as not all species of marine

mammals can be recorded by all types of acoustic sensors, and the frequency capabilities of the

sensor are the greatest limiting factor. Generally speaking, the greater the frequency range of the

system, the greater the number of marine-mammal species that can be detected. This is discussed

further in Section 5.3.4. The recording frequency range of the different sensor packages are

described in Section 5.1.

5.2.8 Data transfer rate

The rate at which data can be transferred will impacts the lag between start of data analysis and

therefore the amount of time it takes to produce results.

Data collected by sonobuoys are monitored in real-time, though data quality can be affected by

sea state, as large waves can potentially wash over the antenna disrupting the radio signal

transmission, and other RF energy can add noise to recordings.

Slocum Gliders have the ability to upload limited data via Iridium satellite link or Freewave

radio4 when they surface, which may also be affected by high sea states when waves may wash

over the antenna causing breaks in the satellite transmission. Data upload via Iridium is

bandwidth constrained therefore lengthy acoustic data files cannot be transferred. Rather, acoustic

data must be retrieved once the glider is recovered. This transfer can be quick, as the recording

device can be removed and attached to a computer once the glider is opened. Newer models may

include data ports that allow for high speed (Wi-Fi or external Ethernet port) connection to

retrieve data files.

Data collected by bottom-mounted sensors can be downloaded only after the systems are

retrieved, and the amount of time to download the data is dependent on the amount of data

collected. For SHARP the recording media may be removed once the pressure vessel is opened.

AMAR data must be downloaded, which can require many hours for large data sets.

4 Freewave is a line-of-sight radio and would require a tender nearby.

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5.2.9 Acoustic analysis

Except for sonobuoys, where first level analysis may be conducted on the recording platform,

acoustic analysis of all PAM data is shore-based. Because sonobuoy data are provided in real-

time, a quick-look analysis of the data (such as identification of regions of the recording with the

presence of different types of sounds) can also be conducted in real-time. More detailed

examination of the data, however, requires shore-based analyses. Provided that acoustic security

issues are resolved, shore-based analysis could be conducted by ADAC (A) or by a civilian

contractor.

5.2.10 Cost

From an operational perspective, the cost of PAM will always be important. The equipment cost

for different types of systems is highly variable, ranging from the least costly, sonobuoys, to the

most expensive, Slocum Gliders. Though the gliders and bottom-mounted sensors are

substantially more expensive than sonobuoys, it should also be kept in mind that gliders, SHARPs

and AMARs are one-time purchases, because these systems can be used over and over again with

relatively minor refurbishment cost. Sonobuoys, on the other hand, are expendable sensors and

repeated purchase of new units will be required. For Table 20, only estimates of the cost of the

systems are provided, operation and maintenance are extra. However, refurbishment (such as

purchase of replacement batteries), maintenance (such as the replacement of broken parts or time

spent determining the cause of a malfunction), deployment and retrieval, and data analysis costs

should also be considered. These costs need to be further investigated to determine the true

operational cost related to each PAM technology.

5.3 Biological considerations

The following sections discuss biological considerations (in relation to cetaceans and their

behaviour) that need to be addressed when choosing acoustic monitoring technologies with

reference to the specific types of sensors and acoustic systems reviewed.

5.3.1 Target species

Cetaceans are classified into two main groups: baleen whales (e.g., blue, fin, sei, minke,

humpback, and right whales) and toothed whales (which include sperm whales, beaked whales,

dolphins, and porpoises). The behaviour and life history patterns, including vocal behaviour and

residency5 on the Scotian Shelf, are generally quite different between baleen whales and toothed

whales. The species of interest (the target species) can therefore have a significant influence on

the type of acoustic system and methods chosen for acoustic monitoring. General differences

between species and the implications for acoustic monitoring are discussed further in

Sections 5.3.3 and 5.3.4.

5 The average amount of time a species spends within a particular area.

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5.3.2 Distribution and abundance of target species

As discussed in the introduction to Section 5, little is currently known about cetacean distribution

and abundance on the Scotian Shelf. Figure 8 and Figure 9 display cetacean distribution for

Species at Risk and other species in and adjacent to the MARLOAs. These figures, obtained from

the MOAMP, are based on sightings data with temporal and spatial gaps in coverage, which may

lead to inaccuracies when compared to the actual species distribution. It is also noteworthy that

the species distributions presented in these figures vary somewhat from some of the species

distributions presented in Breeze et al. (2002). A long-term monitoring effort may help to ensure

that data are more accurate for areas of particular interest.

Long-term acoustic monitoring offers a means of increasing our knowledge of cetacean

distribution and abundance on the Scotian Shelf. It will help identify important cetacean feeding,

breeding, and calving grounds, as well as migration routes. It would take many years to gain an

accurate understanding of annual variability, with a long term payoff of being able to more

accurately predict when and where cetaceans occur, which will help with exercise planning.

Initial acoustic monitoring should focus on MARLOAs where ASW exercises, employing active

sonar, typically occur, or where the RCN may employ active sonar in the future. These areas

include Area G1-C2 (which includes Emerald Basin), G2-H1 (which includes LaHavre Basin),

and Areas L, M and N (which includes waters on the Continental Shelf, Slope and deeper Atlantic

Ocean) (Figure 7). In addition, shallower areas off Halifax Harbour where testing, trials, and

calibration of shipborne and helicopter active sonar are conducted also requires acoustic

monitoring.

Selection and prioritization of MARLOAs for PAM must include concurrence and agreement

with MARLANT exercise planners.

5.3.3 Residency of target species

When and how long cetaceans occur on the Scotian Shelf varies between species. Baleen whales

are typically annual migrants who occur from spring through fall (Breeze et al. 2002). Though

some species are observed in winter months, sightings of baleen whale species are highest during

summer months. Toothed whale species tend to occur within the region on a more year-round

basis, though distribution of species may shift seasonally, with some species tending to move

further offshore during winter and moving back inshore during summer (Breeze et al. 2002).

Most cetaceans are highly transient, as their distribution often correlates to distribution of their

prey (Gaskin 1982; Bowen and Siniff 1999; Stevick et al. 2002), whose distribution are often

associated with ephemeral oceanographic features (such as sea surface temperature and front

formations) rather than static physical features. This means that many species move in and out of

areas over relatively short time scales. The ability of short term acoustic monitoring to predict

presence of cetaceans within an area may be limited. Rather, long-term acoustic monitoring will

be more important for establishing knowledge of variability in cetacean presence within an area

over time to better predict when cetaceans are most likely to occur within that area.

36

Figure 8: Distribution for Species at Risk in and adjacent to the MARLOAs based on

sightings data currently available (taken from MOAMP).

37

Figure 9: Distribution for other species in and adjacent to the MARLOAs based on

sightings data currently available (taken from MOAMP).

38

5.3.4 Vocal behaviour of target species

Several aspects of cetacean vocalizations, including frequency range, source level, and

directionality, will influence the ability to acoustically detect them (Mellinger et al. 2007). Lower

frequency vocalizations (< 1 kHz) can be detected at greater distances than higher frequency

vocalizations. Similarly, louder vocalizations will also travel further distances. Highly-directional

vocalizations will be more difficult to detect when the sensor is off-axis from the vocalizing

animal (i.e. when the vocalizing animal is not facing the sensor). In general, baleen whales

produce lower-frequency vocalizations while toothed whales produce higher-frequency

vocalizations (Figure 10). Larger cetaceans such as baleen whales and sperm whales also produce

louder vocalizations than smaller cetaceans. As well, the echolocation clicks produced by small

odontocetes are much more directional than those of baleen whales and sperm whales (Mellinger

et al. 2007). Baleen whales and sperm whales can therefore typically be detected at much greater

distances than for smaller toothed whales.

The vocalization rate of the target species will also influence the ability to acoustically detect

them. Some species may be nearly silent while others vocalize regularly. Vocalization rates vary

with species, gender, age, and season (Mellinger et al. 2007). An awareness of the vocalization

rate of the target species will help assess the probability of acoustically detecting them if they are

present within an area.

The frequency range of the vocalizations of the species being targeted for acoustic monitoring

also needs to be considered when choosing an acoustic sensor or system to ensure that the system

is capable of recording the species. Generally, the greater the recording frequency range of the

sensor or system, the more species that can be recorded.

39

Figure 10: Spectrum of Atlantic species of interest. Note: vocalizations have not been

published for Sowerby’s, Dwarf sperm whales, and True’s beaked whales.

10 Hz 100 Hz 1 000 Hz 10 000 Hz 100 000 Hz 1000 000 Hz

White-beaked dolphin

True's beaked whale

Striped dolphin

Sperm whale

Short-finned pilot whale

Pygmy sperm whale

Minke whale

Long-finned pilot whale

Humpback whale

Dwarf sperm whale

Cuvier's beaked whale

Common dolphin

Bottlenose dolphin

Blainville's beaked whale

Atlantic white-sided dolphin

Sowerby's beaked whale

Risso's dolphin

Narwhal

Killer whale

Harbour porpoise

Fin whale

Bowhead whale

Sei whale

North-Atlantic right whale

North-Atlantic bottlenose whale

Blue whale

Beluga

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6 Discussion and recommendations

This project provided an excellent opportunity to test current acoustic monitoring and data

processing technologies and processes in order to assess their efficacy and better understand

where improvement is required. This section provides a discussion and summary of what was

learned, along with recommendations for monitoring, exercise planning, and technology

improvement.

6.1 Monitoring plans

Understanding where and when marine mammals occur is necessary for applying mitigation

strategies to scheduling ASW exercises and reducing the impact of active sonar on marine

mammals. To achieve a full understanding of marine-mammal activity in the MARLOAs, all

three of the acoustic monitoring technologies reviewed in Section 5 must be employed, depending

on location, duration and aim of the monitoring.

6.1.1 Long-term monitoring

A significant outcome of the analysis provided in Section 5 was that long-term PAM is required

to improve knowledge of cetacean species distribution and abundance in the MARLOAs. Current

knowledge and data are not sufficient to reliably select areas and schedule ASW exercises with

high confidence that significant interference with marine mammals will be unlikely. As well, it

will be difficult to attribute significant changes in distribution and abundance that are observed

before and after an exercise to the exercise, or to other natural causes without knowledge of

natural variation in distribution and abundance of cetaceans within exercise areas over time.

Long-term marine-mammal monitoring (greater than 30 days) is most effectively completed by

bottom-moored acoustic recorders. This technology is ideal for developing an accurate marine-

mammal database for the MARLOAs including temporal and spatial changes such as differences

between seasons. Ideally, these data would be correlated with relevant oceanography, which may

be correlated to the location of prey and therefore to the presence of cetaceans. This

comprehensive data set could then be used to develop models and allow planners to predict

annual variation based on less-expensive observational data.

Additional analysis should be performed to determine the most-appropriate type and placement of

sensors. For example, if more accurate information on abundance is required, methods for

counting the number of vocalizing animals at any time may be needed (Theriault et al. 2011;

Bougher et al. 2012). These methods may require different sensor configurations, such as those

that support localization and tracking. Selection of systems, sensors, and sampling strategies will

also depend on the primary species of interest, as these affect detection ability and range, and also

may influence sensor configuration.

It is unlikely that 100% area coverage will be obtained during long-term monitoring, especially

for vocalizations that offer limited detection range. Therefore, a strategy that allows for cost-

effective monitoring is required. Given that most cetaceans are somewhat mobile, sensors

distributed with relatively large gaps in coverage may still be effective and should receive further

consideration.

41

6.1.2 Monitoring for specific exercises

Thought long-term monitoring was identified as the highest priority, it will also be beneficial to

conduct pre- and post-trial monitoring. As well, some level of monitoring during ASW activity

will be required to comply with the orders (DND 2008).

Pre-trial monitoring must occur with enough lead time for planners to react, while it must also

occur close enough to the trial for the resulting information to remain relevant. These

requirements may compete in cases where large trials require significant notice, yet transient

species are moving through the areas on relatively short time scales. Another consideration is the

time that it takes to produce information from acoustic data. Analysis processes must therefore be

efficient to be practical.

Pre-trial monitoring would be used to warn trial planners of potential issues (e.g. early migration

of cetaceans into the area), allowing them to adjust trial plans to reduce potential for interference

with marine mammals. Post-trial monitoring could be used to try and measure the impact of the

trial activity on marine mammals, though long-term data would likely be required in order to

determine the significance of observations.

Glider technology is optimized for shorter marine monitoring durations (less than 30 days),

ideally pre- and post-exercise. The DRDC gliders are equipped with onboard processing and

could provide some information in advance of retrieval, which with some advancement could

prove very reliable and useful. Sonobuoys could also be used for short-term monitoring, which

may be practical if coincident with other activities (e.g. rapid environmental assessment

missions). Sonobuoy data could be monitoring onboard the aircraft though most ASW processing

modes are not appropriate for detection of many marine-mammal vocalizations, some of which

are too high in frequency to be audible without additional processing. The only useful technology

for monitoring marine-mammal vocalizations outside of ship borne organic sensors are

sonobuoys during an exercise.

As with long-term monitoring, more analysis would be required to recommend PAM sampling

strategies that would be effective for specific exercises and areas.

6.2 Exercise planning

Pre-exercise and exercise mitigation procedures for planners and ships at sea are extensively

reviewed in (DND 2008). Once a reliable data base for marine-mammal activity is developed for

the MARLOAs, exercise planners will have more confidence in assigning ASW exercise areas

that will avoid concentrations of marine mammals. Until that time, more than one exercise area

must be assigned per event to permit flexibility and reduce loss of valuable exercise time due to

the presence of marine mammals. This strategy, along with effective pre-trial monitoring could be

used to direct resources to the area with the least activity on short notice.

6.3 System/analysis tools and process development

Methods and tools for analyzing acoustic data must be efficient, reliable, and robust in order for

monitoring plans to be effective. Vast quantities of acoustic data must be transformed into useful

42

information during processing. This section provides some recommendations on investment that

would help ensure that the tools and processes are ready to support the overall objective.

6.3.1 Detector testing and improvement

Though this contract provided a good opportunity to exercise the analysis process and find ways

to improve it, no cetacean vocalizations where found in the data. A proper assessment of the

accuracy of the detectors configured for the various cetacean species requires running the

detectors through recordings known to have vocalizations for target species. The next stage in

development of these detectors is thus testing them out on a more biologically rich data set. This

analysis will no doubt lead to a better understanding of the current detector and further ideas for

improvement.

6.3.2 False alarm reduction

The detailed analysis of the detections gave a better understanding of causes of false detections on

the three systems used. Analysts hypothesize that post-processing of the detections would

eliminate many of the false detections, and in particular, writing code to do the following would

substantially help speed up analysis:

Omit detections within the first few minutes of system/sensor deployment, as acoustic data

are often corrupted by deployment noise (e.g. sonobuoy hydrophone run-out).

Omit detections that occurred within the time period that the vessel was approaching the

system for retrieval, as nearby vessels cause significant transient noise and therefore false

alarms.

For a similar reason to the one above, run a vessel detector through the data to determine

areas of relatively loud ship noise and omit detections occurring within that time period.

Omit detections within the last few minutes of a sonobuoy recording, when the radio is

noisy and the buoy is starting to self-scuttle.

Omit any fin whale, sei whale, minke whale, sperm whale, or delphinid detections that occur

without any other detections within a one minute period (e.g. if there is a single sperm-

whale detection then no other sperm-whale detections within a minute before or after that,

eliminate the detection). These species are expected to have a substantially higher

vocalization rate than one pulse/upward sweep/click per minute.

As an alternative, more accurate but also more complex algorithm than the one presented in

the previous bullet; for fin whale, sei whale, minke whale, sperm whale, and the delphinid

detectors, only count detections if a minimum number of detections occur within a specific

time period based on the expected vocalization rate of the target species (e.g. for sperm

whales a minimum of five clicks occurring within a ten second period, half the expected

detection rate for a single animal, in order for a detection to be counted). In other words,

only detect trains of clicks and pulses, rather than individual pulses and clicks.

Develop other species-specific detection filters to help remove false alarms based on

expected vocalization patterns, such as the filter proposed above.

43

For species that produce sets of vocalization, consider performing a more complex analysis

of this set to determine if characteristics such as ICI are consistent with the targeted species.

For this case, generation of sequential time-difference-of-arrival (TDOA) estimates will help

determine if detections are consistent with a single animal, group of animals, or more

random noise. This method would require a sensor array as described in (Bougher et al.

2012; Theriault et al. 2011).

Additionally, testing the detectors on an acoustic dataset which has cetacean vocalizations present

would allow detector settings to be adjusted to maximize the number of present cetacean signals

detected while minimizing the false alarm rate.

6.3.3 ACDC software application

ACDC is becoming a very useful tool for live and post-analysis of acoustic data for detection and

classification of cetacean vocalizations. However, there are various improvements that could be

made to increase analysis efficiency. These include:

Have the sonogram window function more like OPD.

Provide the ability to zoom in and out on the time axis.

Provide the ability to zoom in and out on the frequency axis, or choose a specific frequency

band to examine.

Have the cursor move across time (i.e. track the time) when playing back audio for a file.

Provide the ability to playback audio for a specific or highlighted region of data.

ACDC users would also benefit from a comprehensive user manual, similar to that for OPD.

6.3.4 Software sonobuoy GPS decoder

Some sonobuoys are available with GPS and include NMEA data modulated on the same carrier

as for acoustic data. User of these data requires expensive hardware decoders that will only

monitor on channel at a time. The modulation scheme is relatively simple (binary frequency shift

keying [BFSK]) and a software decoder that could run on recorded data for all channels

simultaneously could be developed. Once completed, it could be provided for free to a number of

units including the Acoustic Data Analysis Centres (ADAC) and the Canadian Forces Maritime

Experimental and Test Ranges (CFMETR).

6.3.5 Ambient-noise monitoring

This analysis reinforced the dynamic nature of acoustic sounds levels. More work should be

performed to assist with visualizing noise levels versus time. Different formats including

compressed spectrograms or energy time indicator (ETI) plots might be used. ETI might be

generated for sub-bands. These formats may also reveal methods for compressing data to

efficiently determine regions of frequent vocalization, as was performed in STAR for

compression of data containing clicks.

44

7 Summary and conclusions

This project was very successful in advancing three objectives:

Improving experience with and knowledge of acoustic monitoring technologies (sensor

systems and analysis software) through a practical test of these technologies. This additional

knowledge and experience may now be applied to improve monitoring plans and the

efficiency and effectiveness of long- and short-term marine-mammal monitoring efforts.

Exercising candidate technologies in order to gain a practical understanding of the strengths

and weaknesses. The knowledge and ideas gained may now be applied to more effectively

using current technology and innovating to generate improvements.

Providing recommendations on how to improve knowledge of cetacean distribution and

density on spatial and temporal scales so that it might be applied in exercise planning and

decision making for mitigation of the impact of operations on marine mammals.

The success was born on the efforts of the Glider 2012 sea trial team, who deployed the

equipment and gathered the data, the data processing and analysis team that worked through these

data to determine sensor system and software performance, and finally on the technology analysis

team that used this information along with external research to provide a practical review of

DND-related and biologically-related issues along with recommendations for a short- and long-

term approach to improving DND’s ability to successfully mitigate impact of operations on

marine life.

The following recommendations should be considered:

Develop and activate a long-term monitoring plan in the MARLOA that are frequently used

for ASW and active sonar. This will improve understanding of spatial and temporal

distribution of cetaceans, allowing for more effective trial planning and mitigation.

Encourage trial planners to include contingency in area selection for ASW and active-sonar

operations and then use short-term cetacean surveys (acoustic and/or visual) near in time to

exercises in order to select the best area for the exercise, with regards to mitigating impact

of operations on marine mammals.

Continue to invest in development of efficient, practical, and robust sensors systems,

analysis processes, and analysis software so that acoustic monitoring can increase in

effectiveness.

45

References .....

Akamatsu, T., Hatakeyama, Y., Kojima, T., and Soeda, H. (1994), Echolocation rates of two

harbor porpoises (Phocoean phocoena), Mar. Mam. Sci., 10 (4), 401-411.

Au, W.W., James, D., and Andrews, K. (2001), High-frequency harmonics and source level of

humpback whale songs, J. Acoust. Soc. Am., 110 (5, Pt.2), 2770.

Au, W.W.L., Kastelein, R.A., Rippe, T., and Schooneman, N.M. (1999), Transmission beam

pattern and echolocation signals of a harbor porpoise (Phocoena phocoena), J. Acoust. Soc. Am.

106 (6), 3699-3705.

Baumgarter, M.F., Van Parijs, S.M., Wenzel, F.W., Tremblay, C.J., Esch, H.C., and Warde, A.M.

(2008), Low frequency vocalizations attributed to sei whales (Balaeboptera borealis), J. Acoust.

Soc. Am., 124 (2), 1339-1349.

Beamish, P. and Mitchell, E. (1973), Short pulse length audio frequency sounds recorded in the

presence of a minke whale (Balaenoptera acutorostrata), Deep-Sea Research, 20 (4), 375-386.

Boisseau, O., Gillespie, D., Leaper, R., and Moscrop, A. (2008), Blue (Balaenoptera musculus)

and fin (B. physalus) whale vocalizations measured from northern latitudes of the Atlantic Ocean,

J. Cetacean Research and Management, 10(1), 23-30.

Bougher, B., Hood, J., and MacKillop, E. (2012), Analysis of Cetacean Data and Algorithm

Development (DRDC Atlantic CR 2012-038), Akoostix Inc., Dartmouth, Nova Scotia.

Bowen, W. D. and Siniff, D. B. (1999), Distribution, population biology, and feeding ecology of

marine mammals, In Biology of Marine Mammals. Reynolds, J. E. III and Rommel, S. A. (Eds.)

pp. 423-484, Washington, D. C: Smithsonian Press.

Breeze, H., Fenton, D.G., Rutherford, R.J. and Silva, M.A. 2002. The Scotian Shelf: an ecological

overview for ocean planning. Can. Tech. Rep. Fish. Aquat. Sci. 2393: x + 259 pp

Busnel, R.G. and Dziedzic, A. (1966), Acoustic signals of the pilot whale (Globicephala

melaena) and of the porpoises Delphinus (delphis) and Phocoena (phocoena). Pp. 607¬646 in:

K.S. Norris (ed.), Whales, Dolphins and Porpoises. University of California Press, Berkeley

Clark, C.W. (1990), Acoustic behaviour of mysticete whales, In J. Thomas and R.A. Kastelein

(Ed.), Sensor abilities of cetaceans: Laboratory and field evidence, pp. 571-584, Plenum Press,

United States: New York, New York.

Delarue, J., Todd, S.K., van Parijs, S.M., and DiLorio, L. (2009), Geographic variation in

northwest Atlantic fin whale (Balaenoptera physalus) song: Implications for stock structure

assessment. J. Acoust. Soc. Am., 125(3), 1774-1782.

46

Department of National Defence (In Progress), MARCORD 46-13 Annex A: Marine Mammal

Mitigation Procedures, Maritime Command Orders, Vol. 3A, Department of National Defence,

Canada.

Edds, P.L. (1988), Characteristics of finback (Balaenoptera physalus) vocalizations in the St.

Lawrence Estuary. Bioacoustics 1(2/3), 131-149.

Erbe, C. (2010), Automatic Signal Detection in Noise using Entropy, In Proceedings of the

Acoustics Week in Canada Conference, 32-33, Toronto: The Canadian Acoustical Association.

Gaskin, D.E. (1982), The Ecology of Whales and Dolphins, Heinemann: University of California.

Goodson, A.D. and Sturtivant, C.R. (1996), Sonar characteristics of the harbour porpoise

(Phocoena Phocoena): source levels and spectrum. ICES J. of Marine Science, 53, 465-472.

Kamminga, C. and Wiersma, H. (1981), Investigations on cetacean sonar II. Acoustical

similarities and differences in odontocete sonar signals, Aquat. Mamm., 8(2), 41-62.

Hood, J. (2011), Introduction to the Software Tools for Analysis and Research (STAR): Tools

and Processes Version 1.0 (AI MA 2011-007), Akoostix Inc., Dartmouth, Nova Scotia.

Hood, J., Bougher, B., Moors, H., and Paitich, O. (2012), Analysis for an Acoustic Survey of

Beaked Whales (DRDC Atlantic CR 2012-061), Akoostix Inc., Dartmouth, Nova Scotia.

Hood, J. and Burnett, D. (2008), Compilation of Marine Mammal Passive Transients for Aural

Classification (DRDC Atlantic CR 2008-287), Akoostix Inc., Dartmouth, Nova Scotia.

Kamminga, C., Stuart, A.C., and Silber, G.K. (1996), Investigations on cetacean sonar. Aqua.

Mamm., 22, 45-55.

Knowlton, A. R., Clark, C. W., and Kraus, S. D. (1991), Sounds recorded in the presence of sei

whales, Balaenoptera borealis. In Abstract of the Ninth Biennial Conference on the Biology of

Marine Mammals, 40, Chicago, IL.

LGL Ltd. Environmental Consultants (2005), MARLANT Op Area Management Plan

(MOAMP), Marine Forces Atlantic, Halifax, Nova Scotia.

McDonald, M.A., Hildebrand, J. A., Wiggins, S. M., Thiele, D., Glasgow, D., and Moore, S. E.

(2005) Sei whale sounds recorded in the Antarctic, J. Acoust. Soc. Am., 118, 3941–3945.

McInnis, J. and Ryan, G. (2011), OPD User Manual Version 1.5 (AI MA 2008-001), Akoostix

Inc., Dartmouth, Nova Scotia.

Mellinger, D.K. and Clark, C.W. (2006), MobySound: A reference archive for studying

automatic recognition of marine mammal sounds, Applied Acoustics, 67, 1126-1242.

Mellinger, D.K., Stafford K.M., Moore S.E., Dziak R.P., and Matsumoto H. (2007) An overview

of fixed passive acoustic observation methods for cetaceans, Oceanography, 20(4), 36-45.

ftp://ftp.pmel.noaa.gov/newport/mellinger/papers/MellingerEtAl07-AcousticCetaceanObservation.pdf




47

Mohl, B. and Andersen, S. (1973), Echolocation: High-frequency component in the click of the

harbour porpoise (Phocoena ph. L.), J. Acoust. Soc. Am., 54(5), 1368-1372.

Penny, K., pers. comm. (2012), Discussions Phil Webster/Mr. Kyle Penny (MARLANT

Formation Environment), 21 March 2012.

Rankin, S. and Barlow, J. (2007), Vocalizations of the sei whale Bal- aenoptera borealis off the

Hawaiian Islands. Bioacoustics, 16, 137–145.

Rasmussen, M.H., Wahlberg M., and Miller, L.A. (2004), Estimated transmission beam pattern of

clicks recorded from free-ranging white-beaked dolphins (Lagenorhynchus albirostris), J. Acoust.

Soc. Am., 116(3), 1826-1831.

Rendell, L.E., Matthews, J.N., Gill, A., Gordon, J.C.D., and MacDonald, D.W. (1999),

Quantitative analysis of tonal calls from five odontocete species, examining interspecific and

intraspecific variation, J. Zoo.(London), 249(4), 403-410.

Schevill, W.E., Watkins W.A., and Ray C. (1969), Click structure in the porpoise, Phocoena

phocoena, J. Mammal., 50 (4), 721-728.

Simard, P., Mann, D.A., and Gowans S. (2008), Burst-pulse sounds recorded from white-beaked

dolphins (Lagenorhynchus albirostris), Aquat. Mamm., 34(4), 464-470.

Steiner, W.W. (1981), Species-specific differences in pure tonal whistle vocalizations of five

western north Atlantic dolphin species, Behav. Ecol. Sociobiol., 9, 241–246.

Stevick, P.T., McConnell, B.J., and Hammond, P.S. (2002), Patterns of movement, In Hoelzel

A.R. (Ed.), Marine mammal biology: an evolutionary approach, pp. 185-216, Blackwell Science,

Oxford.

Taruski, A.G. (1979), The whistle repertoire of the North Atlantic pilot whale (Globicephala

melaena) and its relationship to behavior and environment, In H.E. Winn and B.L. Olla (Ed.),

Behavior of Marine Animals: Current perspective in research Vol. 3: Cetaceans, pp. 345-368,

Plenum Press, United States: New York, New York.

Teilmann, J., Miller, L.A., Kirketerp, T., Kastelein, R.A., Madsen, P.T., Nielsen, B.K., and Au,

W.W.L. (2002), Characteristics of echolocation signals used by a harbour porpoise (Phocoena

phocoena) in a target detection experiment, Aquat. Mamm., 28, 275 -284.

Theriault, J., Hood, J., Bougher, B., and Radcliffe, M., Sorting Out Numbers: How Many

Animals are Clicking?, presented at the Fifth International Workshop on Detection,

Classification, Localization, and Density Estimation of Marine Mammals using Passive

Acoustics, 21-35 Aug 11, Mount Hood, Oregon.

Thompson, T.J., Winn, H.E., and Perkins P.J. (1979), Mysticete sounds. In H.E. Winn and B.L.

Olla (Ed.), Behavior of Marine Animals, Vol. 3: Cetaceans, pp. 403-431, Plenum, United States:

New York, New York.

48

Truscott, R. pers. comm. (2012), Discussions Phil Webster/Cdr Randy Truscott (MOG 5

Commander Submarine Support and former MARLANT Submarine Operating Authority),

1 March 2012.

Watkins, W.A. (1981), Activities and underwater sounds of fin whales, Sci. Rep. Whales Res.

Inst., 33, 83-117.

Watkins, W.A., Tyack P., Moore, K.E., and Bird, J.E. (1987), The 20-Hz signals of finback

whales (Balaenoptera physalus), J. Acoust. Soc. Am., 82(6), 1901-1912.

Whitehead, H. (2003), Sperm whales: social evolution in the ocean. pp. 133-205, The University

of Chicago Press, United States: Chicago, Illinois.

Winn, H.E. and Perkins, P.J. (1976), Distribution and sounds of the minke whale, with a review

of mysticete sounds, Cetology, 19, 1-12.

49

Annex A Cetacean detection recommendations

This section provides information on species that may be present in the Glider 2012 trial area

during February, including information on their vocalizations. It was used to determine

appropriate settings for automated detection.

A.1 Species most likely to be detected

Initial analysis involved determining which species might be present during Glider 2012. A

review of the literature on the distribution of cetacean species on the Scotian Shelf (e.g., Breeze et

al. 2002) was conducted. Maps showing all cetacean sightings, by species that have been

recorded in the Fisheries and Oceans Maritimes Region Cetacean Sightings Database were

examined. Analysts used this information to determine the species that occur most frequently on

the Scotian Shelf and assess which of these species are most likely to occur near the study area in

February/March. Based on the information reviewed, it was determined that the species most

likely to occur on the recordings include baleen whale species (such as fin, minke and humpback

whales, and maybe sei whales), delphinids (such as pilot whales and maybe Atlantic white-sided

dolphins and white-beaked dolphins) and possibly harbour porpoise (Table A-21). These were the

species considered during configuration of the automated detectors.

Table A-21: Assessment of whether cetacean species that frequently occur on the Scotian Shelf

region are likely to be recorded in February/March in the study area.

Species that frequently occur on

the Scotian Shelf

Likely to be heard

in winter? Notes about distribution

Blue whale (Endangered)

Not likely Seasonal migrant (in area May-December) though some individuals remain throughout winter; occur most often along shelf edge but occasionally on the shelf.

Fin whale (Special Concern)

Likely Seasonal migrant (in area in spring, summer, fall) though some individuals remain throughout winter; occur over most of the shelf including near-shore areas.

Sei whale (not listed) Maybe

Seasonal migrant (in area in spring, summer, and fall) though some individuals remain throughout winter; occur most frequently along shelf edge but have been sighted over most of the shelf including near-shore areas.

Minke whale (not listed) Likely Seasonal migrant (in area in spring and summer) though some individuals remain throughout winter; occur over most of the shelf including near-shore areas.

Humpback whale (Special Concern)

Likely Seasonal migrant (in area in spring, summer, and fall) though some individuals remain throughout winter; occur over most of the shelf including near-shore areas.

50

Species that frequently occur on

the Scotian Shelf

Likely to be heard

in winter? Notes about distribution

North Atlantic right whale (Endangered)

Not likely Seasonal migrant (in area June-December); occur most commonly on western portion of shelf such as Roseway Basin; sightings in other areas of the shelf are rare.

Sperm whale (not listed)

Not likely Observed throughout the year; mainly found along the shelf edge but also less-commonly observed on the shelf.

Northern bottlenose whale (Endangered)

Not likely Resident year-round, mainly found in submarine canyons of shelf edge.

Sowerby’s beaked whale (Special Concern)

Not likely Unsure is resident or seasonal species, mainly found in submarine canyons of shelf edge.

Long-finned pilot whale (not listed)

Likely Resident year-round; occur over most areas of the shelf and shelf edge including near-shore areas, although distribution tends to move away from the coast in winter.

Atlantic white-sided dolphin (not listed)

Maybe Seasonal migrant (in area in summer and early fall) though some individuals remain throughout the winter; occur over most areas of the shelf including near-shore areas but tend to move offshore in winter.

Common dolphin (not listed)

Not likely Seasonal migrant (in area in summer), distribution associated with water temperatures > 11oC; occur over most areas of the shelf including near-shore areas.

White-beaked dolphin Maybe Year-round resident (though most commonly observed in fall and winter); occur over most areas of the shelf but especially along the shelf edge.

Harbor porpoise (Threatened)

Maybe Resident year-round; occur mainly in shallower waters (<125 meters deep), but tend to move to deeper waters away from the coast in winter.

A.2 Detector configuration

Table A-22 reviews the vocalization characteristics of the cetacean species that were most likely

to occur on the Glider 2012 recordings. The following detectors were configured using the

settings documented in Section 3.3:

Fin whales: Detector configured to detect low-frequency pulsive calls. Most energy is likely

to be in the 18-25 Hz range. The Mobysound library (Mellinger and Clarke 2006) contains

an example. See file: 93-002-0244.ch11.wav (http://www.mobysound.org/mysticetes.html).

Sei whale: Detector configured to detect low-frequency downward sweeps. Most energy is

likely to be in the 21-100 Hz range, ~1.0-1.3 sec in duration, ~ 1-4 sec intervals (Figure A-

11). No sei whale recordings were readily available.

http://www.mobysound.org/mysticetes.html

51

Figure A-11: Spectrogram of downward sweeps likely produced by sei whales

(Baumgarter et al. 2008)

Minke whale: Detector configured to detect low-frequency pulsive calls. Most energy is

likely to be in the 100-400 Hz range. The MobySound library (Mellinger and Clarke 2006)

for an example. See file 93-02-1238.ch12.wav (http://www.mobysound.org/mysticetes.html).

Humpback whale: Detector configuration had already been developed and tested for

humpback whales (Hood et al. 2008).

Delphinids: Detector configured to detect high-frequency clicks. It is expected that if

delphinid species such as these are in an area they will be producing echolocation clicks,

thus detection of their clicks should be adequate for species detection. The click

vocalizations of the delphinid species of interest have not been well described in the

literature (Table A-22), thus a general high-frequency click detector, likely to detect clicks

produced by any of these species, will be used. Delphinid clicks are generally broadband,

spanning a wide range of frequencies (~1-18 kHz for pilot whales, sometimes up to 100 kHz

for dolphins). The detector will be configured to detect clicks > 6 kHz in frequency.

Duration or these clicks is likely in the 1-4 ms range, and inter-click intervals are likely in

the range of ~50-200 ms. Species will be classified as either ‘pilot whales’ or ‘other

delphinids’ based on the characteristics of the whistles/squeals accompanying the clicks

detected. More particular classification would have occurred through aural/visual

http://www.mobysound.org/mysticetes.html

52

classification on the resulting detections from the high-frequency click detector, but no

detection occurred.

Sperm whales: Though sperm whales are not likely to occur on these recordings

(Table A-21), a reliable sperm-whale detector has already been configured and was used on

the recordings.

Harbor porpoise: It will be difficult to configure a detector to detect harbor porpoise

vocalizations as the frequency of their clicks occur above the frequency range of the

available recording systems and their non-click vocalizations are not well described in the

literature. As this species might occur but is not likely to be recorded, harbor porpoise

presence was not assessed.

Table A-22: Description of vocalizations made by the cetacean species that are most likely to be

recorded in February/March in the study area

Species Type of

vocalization

Frequency range (dominant

frequencies) [kHz]

Further Description References

Fin whale

Clicks 0.016-0.028 Duration: 3 ms/click, 9 s/train

Boisseau et al. 2008 Delarue et al. 2009 Edds 1988 Thompson et al. 1979 Watkins 1981 Watkins et al. 1987

Pulse/ragged pulse

0.018-0.025 (0.02)

Constant call 0.02-0.04

Moans, tones, upward sweeps,

downward sweeps

0.016-0.75 (0.2)

Tonal FM call 0.010-0.31 (0.02-0.15) Duration: 0.2-4.7 sec each

Whistles 2.5-5 (1.5-2.5)

Sei whale

Tonal FM pulses

1.5-3.5 (3.0)

Duration: 4 ms/pulse, 0.5-0.8 s/train Two bouts/trains separated by 0.4-1.0 s 1.4-2.6 s total

Thompson et al. 1979 Knowlton et al. 1991 MacDonald et al. 2005 Rankin & Barlow 2007 Baumgarter et al. 2008 Downward

sweeps 21-100 Hz Duration: 1.0-1.3 s

Minke whale

Clicks 3.3-20 (4.0-7.5) Duration: 0.5-1.0 ms/click Beamish & Mitchell 1973 Winn and Perkins 1976

Pulsive 0.1-0.4 (0.1-0.2) Duration: 0.04-0.07 s/pulse, 1 min/train

53

Species Type of

vocalization

Frequency range (dominant

frequencies) [kHz]

Further Description References

Moans, grunts, downward sweeps,

0.06-0.14

Ratchet 0.85-6.0

Thump trains 0.1-2.0 (0.1-0.2)

Humpback whale

Clicks 2.0-8.2

Au et al. 2001 Clark 1990 Edds 1988 Thompson et al. 1979

Pulsive/pulse trains

0.025-1.8 (0.025-0.08)

Grunts, moans 0.2-1.9 (0.035-0.53) Duration: 0.2-8.2 s

Horn blasts (0.41-0.42)

Shrieks (0.75-1.80)

Songs 0.02 - >15.0 (0.12-4.0) Duration: hours

Long-finned pilot

whale

Clicks 1-18 (6-11) Bushnell & Dziedzic 1966 Steiner 1981 Taruski 1979 Whistles 1-8 (1.6-6.7)

Atlantic white-sided

dolphin

Clicks No description available Steiner 1981

Whistles (6-15)

White-beaked dolphin

Clicks No description available Rasmussen et al. 2004 Rendell et al. 1999 Simard et al. 2008 Squeals (8-12)

Harbor porpoise

Clicks (110-150)

Duration: 0.02-1.5 ms/click, < 2 s/train ICI duration:10-123 ms Rate: < 200-500 clicks/s

Akamatsu et al. 1994 Au et al. 1999 Bushnell & Dziedzic 1966 Goodson & Sturtivant 1996 Kamminga & Wiersma 1981 Kamminga, Stuart & Silber 1996 Mohl & Anderson 1973 Schevill et al. 1969 Teilmann et al. 2002

Harmonic calls 0.1-12 (2) Duration: 0.25-2 s

54


55

Annex B Software Tools

This section provides background information necessary to understand the role that DRDC

software played in this contract. Their relationship to the project is described below while a high-

level description of the tool itself is provided in the subsections below:

SPPACS was used for the data processing described in Section 3, including data formatting,

ambient-noise analysis and automated detection processing.

STAR-IDL was used to perform some post-processing and figure generation including

detection clustering and generation of detection density plots.

OPD was used for data quality assurance and classification of signals.

ACDC was one of the primary tools used for automated detection performance analysis.

Grouped detection were loaded into ACDC and classified.

The PARB was used as a substitute for the Slocum Glider, which experienced serviceability

problems. The PARB and Slocum Glider share a common component for data recording and

processing, the AS.

B.1 Signal Processing Packages (SPPACS)

SPPACS is a group of software programs that are written in the C/C++ programming languages,

with each application providing a specific processing or utility function. They are designed to run

on Linux and OSX based PCs and typically work with Defence Research Establishment Atlantic

(DREA) formatted data files (DAT), though format converters are also contained in the suite.

SPPACS has slowly evolved to its present day state.

The SPPACS software suite consists of two types of software. One type is runtime executables.

These applications have proven to be very useful in simplifying data management and sonar

processing tasks by providing a set of tools from which to build the necessary, often much

customized, processing streams. These streams can be run from the command line or assembled

into scripts to perform batch-processing tasks allowing large amounts of data to be automatically

and incrementally processed.

The second form of the software is a group of library functions that can be used by other

programs to efficiently perform standard tasks. These library functions are extensively used by

the runtime software, but can also be used for other applications, such as OPD. There are several

types of libraries of which three are most commonly used in SPPACS:

Utility (e.g. math, geo, filesystem …) libraries that consist of utility routines for performing

tasks, such as header manipulation, geospatial data representation, and command line

parsing.

Signal Processing (e.g. splib) libraries that contain modules for low-level signal-processing.

A new SPPACS module typically consists of one or more SPLIB modules linked together

with an SPPACS user interface.

56

Sonar Processing (e.g. sonlib) libraries that contain modules consisting of several SPLIB

modules linked internally to create a complex sonar module, such as passive processing.

B.1.1 Background and design information

More generic and reusable software was created by separating the library code above from

SPPACS. These modules are independent of the data header format, time-stamping method, etc.,

and are suitable for integration in real-time processing systems. The libraries can be built to run

on a number of UNIX, OSX or Microsoft Windows platforms and on less common processors

such as the ARM core and Texas Instruments (TI) DSP. Once successfully ported, the CMAKE

build environment supports subsequent builds with a command line option.

The C and C++ elements of the libraries are intentionally separated to ensure that the core

capability, found primarily in the C modules, can be readily ported to systems that don’t support

the more complex language features employed in the C++ version of the libraries. For the most

part, the C++ layer consists of a wrapper on the C layer that provides a more generic method of

instantiating, connecting and running modules. This is provided by inheritance that is, in part, the

adoption of a common interface from a base class allowing parts of the system to interact with a

module without knowing the details of the module. Connection of SPPACS applications using

UNIX pipes provides similar functionality at the application layer.

SPPACS is also supported by a number of libraries, such as the Fastest Fourier Transform in the

West (FFTW), helping to ensure that the SPPACS software runs as efficiently as possible, while

providing a significant reduction in coding effort. These dependencies, and the associated

licenses, are tracked for those projects that require knowledge of intellectual property (IP).

B.2 STAR-IDL

The STAR-IDL6 tools were developed to support general research and analysis objectives at

DRDC Atlantic. The actual software goes hand-in-hand with an analysis process that is intended

to help formalize a reliable and consistent research and analysis methodology. The primary

objectives of the STAR-IDL tools are:

Provide scientific grade analysis tools that allow for efficient, detailed quantitative and

qualitative analysis of a data set.

Provide scientific grade algorithm prototyping and refinement tools that can be used to

quickly realize a variety of algorithm options, validate the basis of the algorithm, and

determine the best approach to use for system prototypes.

Support synergy between DRDC groups and the Department of National Defence (DND) by

providing a common software base for analysis. This synergy encourages inter-group

communication and simplifies user training, analysis process development, documentation

and data portability.

6 The STAR-IDL tools were formerly referred to as the Software Tools for Analysis and Research (STAR).

The STAR Software Suite has now come to mean the greater tool set, including OPD, ACDC, SPPACS, etc.

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Support cost and analysis efficiency by providing software reuse and common tools and data

formats. Examples of efficiency would be using the output of analysis from one group to

feed the inputs of another, or using common software components to lower development

cost of several custom analysis tools.

Most STAR-IDL components are currently implemented using Interactive Data Language (IDL),

though the design is not restricted to IDL. For example, localization algorithms contained in C++

libraries are accessed from IDL.

Applications in the STAR-IDL tools are built using a combination of reusable and custom

components that meet the requirements of each application. The layered design and common

components allow for rapid and logical development of new capabilities. Though currently

focused on two main areas - sonar data processing and analysis, and target localization, tracking,

and multi-sensor data fusion - the tools are capable of expanding to meet other analysis and

research requirements.

B.3 Omni-Passive Display (OPD)

OPD is a standalone signal processing application designed to run on UNIX, OSX, and Microsoft

Windows platforms. It can be used to quickly produce sonogram, energy-time indicator (ETI),

amplitude-line indicator (ALI), and time-series output from DREA digital acoustic tape

(.DAT/.DAT32) files, wave files, sound card, EADAQ, Rapidly Deployable System (RDS), and

Northern Watch. The following functions summarize its capability (detailed information can be

found in the OPD User Manual [McInnis, J. et al. 2011]):

A user can quickly set up the desired signal processing by loading in a preset configuration

from storage, or by simply defining the desired frequency and time resolution. A more

sophisticated user can define a wide range of parameters, including Fast Fourier Transform

(FFT) size, zero padding, overlap, quantization range, decimation, sonogram compression

and much more.

OPD provides an optional beamformer and is capable of processing complex heterodyned

time-series data.

Annotations can be added to the data.

The user can assign a category (or classification) to the annotation from a list of

presets as well as provide free-form text to associate with the annotation.

Previously generated annotations are displayed on screen when processing data

associated with the annotation.

The annotation format is compatible with STAR-IDL and ACDC.

Each processing result is stored in memory and can be selected for viewing and analysis.

Analysis tools include a crosshair cursor for time-frequency measurements.

The entire sonogram can be saved to an image file to capture the output for reports, etc.

A WAV extraction tool allows the operator to define a region within a sonogram and clip

the raw data associated with the selected bounds into a wave file.

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Operational measurement tools such as harmonic, banding, periodic-event, and Doppler

cursors can be used to analyze advanced features in data and learn tactical information about

potential targets.

B.4 Acoustic Cetacean Detection Capability (ACDC)

The ACDC application was developed to provide an initial cetacean monitoring capability for

DRDC with the hopes of growing the application to provide broader, generic support. The vision

is to create a component that can be connected to a variety of sonar systems and configured to

automatically monitor data streams for marine-mammal vocalizations. Eventually detections

would be vetted by more complex classification software before being presented to an operator

for validation and mitigation. An intuitive user-friendly display would allow an operator to

operate the system part-time and automatically log detections with annotation showing mitigation

action. This log information could also be merged with other streams, such as ping logs, to

provide comprehensive evidence gathering to support the crew in the case of an incident.

The software is contained in two separable components; display processing and control (ACDC);

and signal processing (sp_transient_processing). This was intentional and allows signal

processing to take place off-line or in a remote system such as the Slocum Glider, though it can

also be run as part of ACDC. The heart of the detection processing is the Sentinel sonar library

(SONLIB) module that can be tuned for transient detection. Processing results are stored in up to

five formats; American Standard Code for Information Interchange (ASCII) log files, WAV files,

and DREA DAT formatted power files containing black and white GRAM images, power files

containing raw spectral data, energy time indictor (ETI) files containing band vs. time data, and

amplitude line integration (ALI) files containing the averaged spectra data. The detection results

are dynamically read into the ACDC application for operator analysis and verification. Dynamic

reading allows the processing and analysis to run simultaneously, providing automatic updates as

detections are made. ACDC will function on any data set once provided with a directory in which

to find the required detection results.

B.5 Acoustic Subsystem (AS)

The AS is a general-purpose embedded acoustic recording and detection system composed of an

integrated set of reusable software modules. The AS operates in one of three modes:

Transient detection and recording mode: In this mode onboard detection processing is

performed, the entire sample period is recorded, and individual captures (WAV files) are

created for each detected transient along with an ASCII detection log. The maximum

sampling duration on the Viper single board computers is 5 minutes with a duty cycle of

~50% when operating at the 40 kHz bandwidth.

Target (vessel) detection and recording mode: In this mode onboard detection processing is

performed, the entire sample period is recorded, and an ASCII detection log is created. In

this mode, the bandwidth is often limited allowing the AS to process at real-time.

Ambient recording mode: In this mode the AS records acoustic data and operates real time,

so recording duration is not limited on the current hardware, provided enough flash memory

is available.

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The system is intended for soft real-time operation and is normally installed on a low-power,

fixed-point, general-purpose processor and paired with other technology that acts as the vehicle

(e.g. Slocum Glider, PARB, Stealth Buoy).

When used for marine-mammal detection, it is most commonly paired with the Slocum Glider,

where the current acoustic sensor bandwidth (40 kHz) supports detection of a broad range of

species. The AS is designed to work with ACDC, where ACDC provides post-processing – if

required – and data visualization.

The AS is composed of a number of technology layers. Its capability will be described at each

layer, as capability varies significantly. At the topmost layer, the AS is:

Single channel

Data is sampled at 16-bit resolution at rates up to 100 kHz

Acoustic bandwidth is variable from 2 kHz – 40 kHz

Acoustic preamplifier gain is variable from 0 dB – 35 dB in 5 dB steps. The A/D also has

adjustable input voltage ranges that are ± 1, 2, 5 and 10 Volts.

Data is recorded on a standard Compact Flash card and recordings can be taken up to the

limit of the card capacity

The AS provides a serial interface for a host controller interface with a basic command set

to allow an external system to control it. The extensible command set includes control of the

sample period, time setting, query of detection status, and power off. Where an external

interface is not available the same interface is controlled by an AS host controller via a

socket and onboard software. The AS host controller provides additional control and

functionality over a serial user interface (terminal interface).

The AS provides a serial user interface (terminal interface with text menus) for direct user

access to manage modes, logs, data, etc.

Underlying the AS is modular technology with greater potential. It allows for processing of any

number of data channels at any sample rate, using floating-point numbers. This includes both

detection processing and recording. This software is written so that any data format can be

supported via an appropriate module at the front of the processing stream. WAV, WAV64, and

other formats supported by libsndfile are currently supported along with a number of DRDC

proprietary formats, one PC-104 A/D, and various soundcards that are supported by standard API

on MS Windows, OSX, and Linux. New data sources are regularly added.

B.6 Passive Acoustic Reusable Buoy (PARB)

The PARB is a floating buoy that contains the AS along with a host controller (AS host), which

assumes responsibility for controlling the system, replacing the host interface normally provided

for the Stealth buoy and Slocum Glider to control the AS.

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The PARB provides an important stepping stone for system development, as there is less impact

if the electronics fail and it has more room and options for modification and live monitoring (i.e.

all electronics are under DRDC control, it is always on the surface, it can be quickly found using

radar, and it provides a live RF link). The PARB can also serve as a prototype for intelligent

sonobuoy development.

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Annex C Software enhancements

This section documents software development performed under this contract. These efforts also

made use of new methods for cetacean detection filtering and clustering (Bougher et al. 2012),

which were used to increase the analysis efficiency with ACDC, as described below. In addition

to upgrading ACDC to handle grouped detections, general analysis-related upgrades were

implemented.

The following points summarize significant development:

ACDC was upgraded to use detection summary messages instead of transient messages as

the primary source of information for associating data snippets to detection. This allowed

for grouped-detection analysis using post-processed logs from STAR-IDL. This upgrade

also allows for an arbitrary length message to be displayed before and after detection.

NOTE: Significant scripting and formatting capability was developed to drive this process.

Functions that generate the data extracts and re-write the patched detection logs are located

in detect_formatting_support.pro in the main_idlprog subdirectory. The TRANSIENT

messages contained in the original detection logs were still required for parsing purposes,

and were added to the grouped detection logs using filter_detsum_messages.py contained in

the main_scripts directory.

ACDC was upgraded to allow used to launch external applications to view and analyze

WAV files associated to detection. Audacity was used for analysis under this contract,

which improved the analyst ability to listen to and classify signals.

ACDC was upgraded to render either 1-bit (2 level) or 4-bit (16 level) grey-scale

spectrogram display, allowing for higher quality sonograms for operators to analyze.

A stop button was added to ACDC to allow users to cease audio playback.

A new plot was added to STAR-IDL that renders detection data to detection-density plots

showing one plot for each detector configuration and an indication of when data were

present for analysis.

Scripts for analyzing ambient-noise statistics were upgraded to provide a movie mode where

each of the spectra is plotted in succession along with an updated time.

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63

Annex D Configuration management

The final software deliverable for this contract was provided on the STAR release CD, which

generated and delivered to James Theriault, DRDC’s lead scientist for STAR, on 30 March 2012.

The release coincides with delivery for several STAR DISO call-ups. This section of the

document describes the content of that CD.

D.1 STAR branch and release information

Each logical grouping of software modules has been independently versioned on the CD that is

also versioned. The current STAR release version is 6.6.11 and contains the following:

OPD 2.6.0

ACDC 2.1.8

SPPACS 1.3.0

Analysis Tools 6.13.0 (STAR-IDL)

Installation instructions are located in the root directory on the release CD.

D.1.1 STAR software documentation

Some manuals, API documentation, and other design documents are provided with the software

release CD. In a standard STAR distribution they can be found by opening the

/usr/local/atools/star-6.6.11/documentation.html file in a standard web browser. This page

contains links to several sets of documentation including:

Software revision history

Software API documentation including, IDLDoc for the analysis tools (STAR-IDL) and

DOxygen generated documents for OPD, ACDC, and SPPACs

The STAR user manual7

STAR quick reference guides

STAR-IDL application user manuals

STAR application user manuals

Useful third-party Documentation

7 This manual has not been updated for some time and is in the process of being superseded by newer

documentation included on this CD.

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D.2 Issue summary

The issue summary in Table D-23 shows the current state of known defects for all of the software

release candidates listed in D.1 as of 30 March 2012.

The distribution of issues is indicative of the maturity of the software. Though maturing, much of

this software is composed of various evolutions of an iterative design, especially command-line

SPPACS applications and STAR-IDL components. This software would benefit from general

design improvements and refactoring. There are no active blocker issues but there are several

critical issues. These are obscure or infrequent bugs that were discovered during current or past

work, but budget or schedule has been insufficient to address them yet. Critical issues are issues

that still allow the operator to perform their function but could cause erroneous results or loss of

data in those instances. These bugs should be fixed in the near future. Only Blocker issues do not

have a work-around and need to be addressed before a contract can be completed successfully.

Table D-23: Issue summary (severity vs. status) for all software on STAR release 6.6.11

Opened Reopened Resolved Closed

Blocker 0 0 0 37

Critical 12 1 0 75

Major 113 4 1 224

Minor 40 2 0 25

Trivial 7 0 0 3

Undecided 0 0 0 5

Table D-24 summarizes critical issues that remain open, but only for software relevant to this

contract. None of the critical issues had any effect on the success of this contract. Resolution of

these issues may increase efficiency during the execution of future call-ups or contracts.

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Table D-24: Known critical issues for STAR

Module Issue ID Summary Description

OPD OPDY-275

Crash on deleting data A crash was experienced while deleting old data. The user was processing data when the user commanded the deletion, but it appears that processing and deletion were completed at the same time, as the data being processed was also deleted, which may only occur once processing is completed. An OPD crash resulted. Effort to reproduce the crash has been unsuccessful, reinforcing that the user may have gotten the timing exactly right, deleting the data at the exact time that processing completed. See comments for system dump.

OPD OPDY-245

OPD crashes if EADAQ changes state during processing and OPD hasn’t stopped processing

OPD will hang if the user stops processing and EADAQ resets. The display will freeze and the user is unable to stop processing.

OPD OPDY-244

OPD crashes when stopping processing after EADAQ stops

OPD will crash if the user tries to process EADAQ when EADAQ is not recording.

OPD OPDY-77 OPD hangs when validating data sources

OPD tries to verify the data stream by reading as much data as required to determine the format and sensors. This stalls the system until it receives this information. An array server can be accepting connections but not sending data, and OPD connects to the Northern Watch array server automatically as soon as a valid IP and port are given. OPD even saves the IP and port to save users from retyping them each time. If the server stops feeding data on one port (but still allows connections) and starts another, and the user restarts OPD, OPD tries to connect to the previously saved port and hangs. Workaround: 1. Port being saved is in the registry settings. It can be changed manually before resetting OPD or 2. The user can start the array server on the previously saved port. This will free OPD from the hang. Permanent fix (tentative): 1. Prevent OPD from validating live data sources automatically by delaying validation until a connect button is activated. 2. Use a timeout when validating sources so that failure to connect on a socket causes a failure when the timeout period is exceeded. This solution will not detect the case where the connection is active, but data is not available

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SPPACS AKSP-72 sp_median_nrmf is referencing a null pointer occasionally in win32

sp_median_nrmf causes a crash (in OPD) by dereferencing a null pointer. This seems to only happen on win32, but may just be hidden in Unix environments (Windows has a history of being more strict, especially in debug mode). Before the crash (during construction of the module) warnings are printed to stderr, "Can't find remove point". Examination of the code revealed a comment indicating that the logic should not allow for this case, proving that this is a bug.

STAR-IDL AKOT-153 Animation with selected tracks-bad behaviour

When a user selects tracks in ITAC then starts animation, strange tracks were observed. If the user turns off selections, the animation behaves normally again.

STAR-IDL AKOT-107 Ownership of overlays and contained data

There is a general problem with ownership of overlays and contained data by the tactical plot. The tactical plot can be closed and reopened several times during an application's lifetime, so if it destroys all data that it contains it will be lost to the application and cannot be used on subsequent instantiation of the tactical plot, or usage by other modules (i.e. tracker). It may be reasonable just to own the overlay itself and not the contained data, but then ownership needs to be assigned to something. Another option may be to notify the tactical plot when it owns an overlay. Currently image overlays are owned by the tactical database, because it would be more effort to create a data container in the database and then force the creation of an overlay after the fact. This may need to change depending on how this issue is resolved. If two objects are created (image container and image overlay) then caution must be exercised when data is passed between them to avoid expensive data copies for large images.

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SPPACS AKSP-49 Log entries for sp_transient_processing need to be adjusted

The attached (parsed to Excel) log example can be referenced to help understand the issues. The following items need to be addressed: 1. The log entry is of type SENTINEL though there are really 2 logs types SENTINEL and TRANSIENT. A SENTINEL log entry doesn't include a capture filename as it doesn't do any extraction. Currently the capture filename is listed as UNKNOWN when sp_sentinel is run on data. It would be preferable that they are two separate logs types (TRANSIENT would be SENTINEL with Capture Filename [the only entry in a transient_data segment] appended to the end of the log). 2. At the customer’s request, change UNKNOWN to SENTINEL for ping. Make display type AUTO for sp_transient_processing. 3. At the customer’s request, allow us to set a string as the recorder for sp_transient_processing. In this case it is to be referenced to the dataset (Mobysound, Q302, etc.). If not provided make it NA. 4. Add an RF entry. It should be year, channel, RF, beam, file. RF is really the sensor ID or name, which is different than the recording unit or file (naming will need to be adjusted as RF is legacy to buoy data IDs), as it is specific to that channel. Make it NA for now. Later the option will be added to specify it to the processing. 5. Add a placeholder (or ideally the real thing) for the band name to go after the band number in the log.

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69

List of symbols/abbreviations/acronyms/initialisms

A/D Analogue to Digital

ACDC Acoustic Cetacean Detection Capability

ADAC Acoustic Data Analysis Centre

ALI Amplitude Line Indicator

AN Ambient Noise

API Application Programming Interface

ARM An incorporated technology company

AS Acoustic Subsystem

ASCII American Standard Code for Information Interchange

ASW Anti-Submarine Warfare

AUV Autonomous Underwater Vehicle

AUX Auxiliary

BFSK Binary Frequency Shift Keying

C2 Command & Control

CD Compact Disk

CFMETR Canadian Forces Maritime and Experimental Test Ranges

CM Configuration Management

CMAKE A cross platform build tool

CO Calibrated Omni

COTS Commercial Off The Shelf

CR Contract Report

CSA Contract Scientific Authority

CSV Comma Separated Value

DAT DREA Data Format

DAT32 32-bit version of DAT

dB decibel

DFO Department of Fisheries and Oceans

DIFAR Directional Frequency and Ranging

DISO Departmental Individual Standing Offer

DND Department of National Defence

DOxygen An inline documentation generator

DRDC Defence Research and Development Canada

DREA Defence Research Establishment Atlantic

DSP Digital Signal Processing/Processor

EADAQ Environmental Acoustic Data Acquisition

EPS Encapsulated post-script

ETI Energy Time Indicator

FFT Fast Fourier Transform

FFTW Fastest Fourier Transform in the West

FM Frequency Modulation

GB gigabyte

GPS Global Positioning System

GRAM Sonogram

Hz Hertz

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ICI Inter-Click Interval

ID Identification

IDL Interactive Data Language

IDLDoc IDL Documentation (similar to Doxygen)

IP Intellectual Property

ITAC Integrated Tracking and Aural Classifier

kHz Kilohertz

m Metre(s)

ms millisecond

MARCORD Maritime Command Order

MARLANT Maritime Forces Atlantic

MARLOA MARLANT Operational Area

MOAMP MARLANT Operational Area Management Plan

MS Microsoft

NAD Non-Acoustic Data

NMEA National Marine Electronics Association

NO Number

NS Nova Scotia

OPD Omni-Passive Display

OSX Operating System X (Ten) - Apple OS

PA Project Authority PAM

PARB Passive Acoustic Reusable Buoy

PC Personal Computer

PC-104 An embedded PC standard

PCM Pulse Code Modulation

PPS Pulse Per Second

PWGSC Public Works and Government Services Canada

R&D Research and Development

RCN Royal Canadian Navy

RDS Rapidly Deployable Systems

RF Radio Frequency

RHIB Ridged Hulled Inflatable Boat

SHARP Subsurface High-fidelity Audio Recording Packages

SONLIB Sonar Library

SOW Statement of Work

SPLIB Signal Processing Libraries

SPPACS Signal Processing Packages

STAR Software Tools for Analysis and Research

STAR-IDL IDL specific applications of STAR

TDOA Time-Difference of Arrival

TI Texas Instruments

TOC Table of Contents

TS Target Strength

UNIX A computer operating system

VDS Variable Depth Sonar

VHF Very High Frequency

WAV Wave file format (i.e. .wav)

WAV64 64-bit Wave format

Documents

Data Analysis for Underwater Environmental Acoustic