Digital Noise Reduction: Understanding Lab and Real World Outcomes Ruth Bentler University of Iowa

Digital Noise Reduction: Understanding Lab and Real

World OutcomesRuth Bentler

University of Iowa

Analog NR (1980-90s) Early spectral approaches

Switch ASP (means low frequency compression) Adaptive filtering Frequency dependant input compression Adaptive compressionTM

Zeta Noise BlockerTM

Today’s versions Most are modulation-based with some algorithm for

where and how much gain reduction should occur; At least one other (Oticon) first introduced a strategy

called “synchronous morphology” to determine when noise reduction will occur;

Several are now implementing Wiener filters as well Many also use some mic noise reduction, expansion,

wind noise reduction, and even directional mics as part of the strategy they promote.

Today’s talk Focus on DNR Defined here as modulation-based noise

reduction Difficult to “un-involve” the other noise

reduction approaches currently implemented Circuit noise Wind Noise etc

Let’s focus on the impact of Wiener filtering…

Norbert Wiener, Missouri-born theoretical and applied mathematician; developed filter in the early 1940s, published in 1949

VERY interesting fellow….

Let’s focus on the impact of Wiener filtering… The input to the Wiener filter is assumed to be a

signal, s(t), corrupted by additive noise, n(t). The output, x(t), is calculated by means of a filter, g(t), using the following convolution: x(t) = g(t) * (s(t) + n(t))

…where s(t) is the original signal (to be estimated) n(t) is the noise x(t) is the estimated signal (which we hope will equal s(t)) g(t) is the Wiener filter

With DNR shut off, can observe the “onset” of the Wiener filter (~ 3 sec)

Let’s focus on the impact of Sound SmoothingTM…

Intended to reduce negative effect of short transient sounds, such as a door slamming, or cutlery clattering;

Steepness of the envelope slope used to determine if speech or noise (both have crests or peaks)

Very fast time constants; across multiple channels Evidence to support use (Keidser et al, 2007)

How do ‘classification systems’ fit in here?

Many high end products have what are referred to as “classifiers” to categorize the environment for feature activation;

The classification process is likely to impact the onset of many features, esp DNR automatic/adaptive mic schemes Other speech enhancement strategies

Back to modulation-based DNR Modulation count

Important for speech? Typical of noise?

Modulation depth Plomp studies 0-100%

Time waveform of a random noise

Time waveform of a sample speech signal

Modulation spectra

Speech

Noise

Example of algorithm “rule #1”

Example of algorithm “rule #2”

Frequency (Hz)

125 250 500 1000 2000 4000 8000

Diff

eren

ce (

dB,1

/3oc

tave

)

-25

-20

-15

-10

-5

0

5

GN ReSound (CANTA 770-D)

a

Frequency (Hz)

125 250 500 1000 2000 4000 8000

Diff

ere

nce

(dB

,1/3

octa

ve)

-25

-20

-15

-10

-5

0

5

ICRA SpeechRandom NoiseBabble

Starkey (AXENT II AV MM)

b

Siemens (TRIANO 3)

Frequency (Hz)

250 500 1000 2000 4000 8000

Diff

eren

ce (

dB, 1

/3 O

ctav

e)

-12

-10

-8

-6

-4

-2

0

2

SIREN TRAFFICDINING

Unitron (CONVERSA.NT MODA 10A)

Frequency (Hz)

125 250 500 1000 2000 4000 8000

Diff

eren

ce (

dB S

PL

RM

S)

-10

-8

-6

-4

-2

0

2

4

SNR00 SNR05 SNR10SNR15

a

Sonic Innovations (INNOVA)

Frequency (Hz)

125 250 500 1000 2000 4000 8000

Diff

eren

ce (

dB S

PL

RM

S)

-10

-8

-6

-4

-2

0

2

4

SNR00 SNR05 SNR10SNR15

b

Oticon (ADAPTO)

Frequency (Hz)

125 250 500 1000 2000 4000 8000

Diff

ere

nce

(d

B,1

/3o

cta

ve)

-25

-20

-15

-10

-5

0

5

a

Starkey J13 Axent AV75 dB

--SPEECH,RANDOM, MUSIC--

Frequency(Hz)

125 250 500 1000 2000 4000 8000

DIF

FE

RE

NC

E (

dB

,1/3

oct

ave

)

-25

-20

-15

-10

-5

0

5

GuitarPianoSaxophone with background musicRandom NoisePlain Speech

DNR: What happens in the time domain?

Siemens (Triano)

Starkey (Axent)

Widex (Diva)

Sonic Natura 2 SE BTE DIR 50dB Flat Loss

NOISE REDUCTION: HIGHOmnidirectional

EXPANSION: OFF 85 dB Speech+ Random+Speech

(0:57,1:52,2:51)

Average RMS power: -43.79dB




EXPANSION: OFF85dB Speech+ Random+Speech

(0:57,1:52,2:51)

Reduction: 4.25dB

Average RMS Speech= -43.79dB

Average RMS Noise= -48.04dB



EXPANSION: OFF 85 dB Speech+ Random+Speech

(0:57,1:52,2:51)

STARKEY Axent AV 50dB Flat Loss

NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Random+ Speech (0:58,1:53,2:51)




NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Ramdom+ Speech (0:58,1:53,2:51)


NOISE MGMT:MAX Omnidirectional EXPANSION OFF FEEDBACK OFF 85dB Speech+ Random+ Speech (0:58,1:53,2:51)

Reduction: 13.56dB





Reduction(actual increase)=-2.95dB



Data?

Data? Walden et al (2000)

Single-blinded, within subject, crossover design 40 HI subjects

Omni versus directional versus directional + NR

Self reported: Speech understanding: NR+D = D = O Sound quality: NR+D = D = O Sound comfort: NR+D > O

Bottom line: Sound comfort evidence

Data? Boymans & Dreschler (2000)

Single-blinded, within subject, crossover design 16 subjects Lab data: NR = No NR Field trials of 4 weeks (APHAB)

All subscales: NR = No NR Three aversiveness questions: NR> No NR

Bottom line: Some reduced aversiveness

Data? Alcantara et al (2003)

Eight experienced HI HA users wore new aid for 3 months

No improvement for SRTs; no decrement for sound quality while listening to four different kinds of background noise, all in lab

Bottom line: No reduction in sound quality

Data? Ricketts & Hornsby (2005)

14 adults, single-blinded, lab data only 2 speech-in-noise conditions

71 dBA speech, +6 SNR 75 dBA speech, +1 SNR

No effect on speech perception Bottom line: Significant preference for DNR

sound quality in lab (forced choice)

Bentler et al (2007) Lab and field study

25 subjects 3-4 weeks field trials with 4 conditions of NR

Fast onset (~4 sec) Medium onset (~8 sec) Slow onset (~16 sec) Noise reduction turned off

Another 3-4 weeks (with “paired comparison”) of three time constants accessed by memory button

Bentler et al (2007) AV (Aversiveness) subscale showed unaided

and NR-off to be significantly different (i.e., unaided and NR-on had similar aversiveness scores)

Diary entries indicate easier listening Bottom line: Less aversiveness and easier

listening relative to DNR-off, both in lab and in field

Examples from diaries: #05

Off: Traffic, TV too loud On: Could hear in conversations with 20 people

#07 Off: Environmental sounds quite loud and did not notice

with other settings On: Seem to have less background noise

#09 Off: Difficult to hear in noise On: Could hear husband in restaurant and understand

almost everything #12

Off: Background and outside noises seemed louder & overpowering

On: Aid seemed to filter out noises almost to the point that

conversation was too low.

What about kids? Current study underway to assess impact of

DNR on novel word learning, speech perception, and sound quality in young children (ages 4-10)

Evidence (in adults) that novel word learning not impaired (Marcoux et al. 2006)

These and other data summarized: Each company has their approach

Often determined by own philosophy Confined by other features (“overhead”)

The outcomes of those different approaches are very different in both the frequency and temporal domains

Does not appear to alter sound quality, speech perception or word learning

Probably makes listening easier Need to verify DNR performance!

Is it functioning as intended?

Is it functioning as intended?

So what’s a clinician to do? Know your product (whose responsibility??) Verify performance

Probe mic measures of gain/output, watch speech, magnitude and frequency distribution of the gain reduction. Same is possible (maybe even necessary) in the test box.

Also can use music passages, babble noise, etc, to observe effect

LISTEN, listen, listen…

Questions?

Documents

Digital Noise Reduction: Understanding Lab and Real World Outcomes Ruth Bentler University of Iowa