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2011 Spring Noise Conference, Banff Page 1

SSSSH! – Using a Home Theatre System and Other Devices to Provide a Low‐

cost, Noise Cancelling Solution for Noise Pollution Issues in the Urban Home

Michael Smith(a), Matthew Heisie(b), Luithardt Wolfram(c), Manuel Aebischer(d), Emily Marasco(e)

(a) Department of Electrical and Computer Engineering,

University of Calgary, Alberta, Canada T2N 1N4, Email: mike.smith @ ucalgary.ca

(b) Division of Engineering Science, University of Toronto, Ontario, Canada M5A 2N4, Email: matt.heisie @utoronto.ca

(c) Department of Electrical Engineering. Ecole d’Ingeniers et d’architectes de Fribourg, Fribourg, Switzerland. Email: Wolfram.Luithardt @hefr.ch

(d) Department of Electrical Engineering. Ecole d’Ingeniers et d’architectes de Fribourg, Fribourg, Switzerland. Email: [email protected]

(e) Department of Electrical and Computer Engineering,University of Calgary, Alberta, Canada T2N 1N4, Email: eamarasc@ ucalgary.ca

Abstract

Noise pollution in an urban environment can be problematic. Obvious problems to solve are (A) locating the noise source and (B) negotiating to have it stopped. However, an immediate problem in noise situations such as the Calgary “Ranchland’s Hum” is (C) how does the home owner cope with the noise pollution until solutions for (A) and (B) have been found? Of particular concern is sleep deprivation, i.e. noise in the bedroom. In this paper, we present an overview of a proposed, low‐cost, noise‐cancelling solution based on using a home theatre sound system, microphone(s), and some digital signal processing (DSP) code. The audio capture and control program is already in place on Analog Devices SHARC (ADSP‐21469) evaluation kits used in undergraduate embedded system design courses at the Department of Electrical and Computer Engineering, University of Calgary. However the appropriate DSP code design is more of a challenge. For example, if the Ranchland’s Hum is caused by a distant source, then the sound in the bedroom can be approximated as a plane wave. The problem then becomes one of generating compensating anti‐sound using an array of speakers driven from a home theatre system. However, these speakers, and virtual sound sources caused by wall reflections, are in close proximity to the sleeper and can be expected to generate multiple spherical anti‐noise wave fronts rather than the desired planar signal. In this article we look at this and other anticipated problems in solving a variety of noise cancellation situations; home construction and wind turbine noise together with that dreaded scenario, the neighbour learning to play the alpine horn. We show why passive hearing aids consisting of an ear mould and a zero‐gain, all‐pass amplifier might reduce the health issues associated with many noise problems, and how to use SSSH! to hunt down the Hum.


Introduction

There is a 1960’s science fiction story describing a futuristic world where advertizing has gone mad. As

you approach the parking lot, small flying mechanical birds attach themselves to your car window with

suction cups. The birds broadcast adverts to you by vibrating the car windows to produce a loud‐

speaker. Wandering into a store, video cameras recognize you and then generate custom audio adverts

using your personnel information stored in a world‐wide data base. You walk out of the store into the

Mall area, bombarded by sounds when suddenly everything goes quiet. After two minutes you hear the

message “This quiet zone is brought to you by Dr. Smith and his students at the University of Calgary”.

Improbable? Not really, the noise pollution problem already exists today from such varied sources. (A)

When people come home tired from a long day’s work, their night’s sleep is disturbed by a distant

industrial noise source (e.g. The Ranchlands Hum [CTV, 2010; Ranchlands, 2011] or a distant wind farm).

Even traffic noise has been linked to adverse health effects, including an increased risk of cardiovascular

disease [Somers et al, 2008]. Perhaps (B) you are home schooling your kids, or in bed recovering from

an operation, and your neighbours decide to spend the next two months adding a second floor to their

house. Sharp noises such as hammer blows, shouting workers, and impact tools will disrupt the

schooling and slow the recovery of a patient [Kruppa, 2004]. (C) You are a parent worried about the

noise levels in the incubator and around the neonatal ward where your premature child is recovering.

Some international visitors said that another North American urban noise problem (D) the neighbour’s

children practicing in a rock band with drums, is never a problem in Switzerland. Instead they insisted,

with a smile on their faces, its more about (E) yodeling and blowing the Alpine Horn. Actually cancelling

the sound of the Alpine Horn would be a technical challenge because of the low frequency of the notes.

These are all situations that were brought up by students when one of the authors (MS) mentioned in

class one day about being exhausted because “something” had been waking him up around 3:30 AM

nearly every morning for the past two months. So the question was asked – Could you, at least in

principle, use the digital signal processing software and hardware from embedded systems courses at

the University of Calgary [Smith, 2004 to 2011] to produce an inexpensive, but effective, noise

cancellation system. This paper is based on that premise:

There is a noise annoying you in your home. You propose to

Capture the sound with a series of micro‐phones, and then

Feed the captured audio into a small ‘inexpensive’ computer system, perhaps your lap‐top,

Analyze the sound so that you can

Output the analysed sound product in the form of an ‘anti‐sound’ signal through the loud‐

speakers of your existing home theatre system to cancel out the noise.

Figure 1 shows the schematic for the proposed SSSH! – SHARC‐based Sound Suppression system for the

Home. Remember, we don’t want to produce a commercial quality product capable of industrial scale

noise cancellation. At this point in time, we just want to be successful enough to create a small Quiet

Zone in a 1 m x 1 m x 1 m (3’ x 3’ x 3’) volume around the sufferer’s head in the bedroom. To keep costs

low, the design requirements include using “equipment most people already have in their house”.


However, because most people will do almost anything to get a good night’s sleep, the project permits

the spending of up to $500 on special parts if necessary.

This paper is organized as follows. The next section provides brief information on an existing audio

capture system capable of providing ‘an ideal demonstration of noise cancellation’. This is followed by a

discussion of what some of the obvious issues that must be overcome to make this system work in the

home. The next sections go into the details of the case studies discussed above, each with its unique

noise cancellation problems and solutions. The final section provides a summary and proposes how to

use the SSSH! to hunt down the Ranchlands Hum.

The Hardware and Software needed for SSSH!

A noise source is preventing your sleep. SSSH! – a SHARC‐based Sound Suppression system for the Home

‐‐ is shown schematically in Figure 1. An array of micro‐phones sends audio signals to an Analog Devices

SHARC evaluation board. This board’s ADSP‐21469 processor (Figure 2A) is specifically designed for high

speed digital signal processing (DSP) calculations needed to compensate for room echoes, time delays

etc that are commonly experienced in home theatre applications. The board can handle 4 input audio

channels. There are amplifiers present on the 21469 board so that the 8 output audio channels can

directly drive speakers. However, in Figure 1, a home theatre system amplifier is used to further boost

these audio signals. Further amplification would be particular useful to drive any ‘woofers’ needed to

cancel out low frequency noise sources.

Software packages (threads) are being activated and de‐activated as needed by a TTCOS scheduler

(time‐triggered co‐operative scheduler [Pont, 2005]) modified to run on the SHARC [Smith, 2004 to

2011]. One thread is responsible for collecting noise source sound samples into input audio buffers, and

for outputting corrective signals stored in output audio buffers. This thread must run with the highest

priority to ensure that all the sound samples are input or output at ‘exactly’ at the correct time to avoid

introducing audio distortions. This high priority is obtained by pre‐empting, or switching out, all other

threads. An analysis thread, which will be described in detail in the next sections, indentifies the noise

characteristics which are used by a third thread to generate the anti‐noise signal. A fourth thread, not

shown in Figure 1, allows the user to control, or tweak, how the system works.

All the signal analysis, control and user interface threads, together with sound capture and generation

threads, could be made to run equally well on a lap‐top running XP or Windows 7. The only trouble is

most people’ s lap‐tops only have one microphone input and a stereo output capable of activating two

speakers without special equipment. We felt this did not give us enough flexibility for the prototype


Figure 1: Schematics of SSSH! – SHARC‐based Sound Suppression system for the Home. An array of microphones

picks up a noise source. The audio information is fed into an ‘inexpensive’ Analog Devices SHARC evaluation board

designed to handle consumer home theatre demonstrations. Information about the noise source is calculated: e.g.

direction, type of sound, etc. An appropriate series of anti‐noise signals are calculated and then fed into a home

theatre system. This feeds the audio signals to a series of ‘standard’ speakers to generate a Dr. Smith’s Quiet Zone

over a small area, such as the head of a bed.

noise‐cancellation system. Later perhaps, we can switch; one student group [Kotchorek et al., 2010; Kotchorek et

al., 2011] developed a cell phone application that captured and analyzed snores. Cancelling the sound of snores

would be a very interesting 5th application of SSSH!, requiring a name shift to SSSSSH! ( ).

Theory and issues around practical noise cancellation

The sound quality test button on home theatre amplifiers is a clear indication that manufacturers know

that many of their customers ‘accidentally’ experience noise cancellation during installation. Sound from

‘centre stage’ of a performance comes out with the same intensity from both left and right speakers.

Switching one set of speaker wires during installation causes the sound from one speaker to be 180o out

of phase to that of the other speaker, so that ‘zero sound’ is perceived by the listener. Actually the

sound would be severely muffled rather than zero; it would only be zero if the device cancelling the


Figure 2: (A) The SSSH! System is implemented on the SHARC 21469 evaluation board which has multiple audio

ports and a high speed processor. The impact of changes in sound intensity with distance means that noise

cancellation can be expected to be more effective over a larger volume if (B) the ‘anti‐sound’ generator can be

placed close to the noise source rather than (C) at a distance from the noise source. (D) Being able to cancel out

one frequency component of a noise source does not automatically cancel out the other frequency components.

sound was in exactly the same location as the item producing the sound; something that is not

physically possible. Fig. 2B shows the principle behind using this approach as an electronic silencer for

an industrial compressor. A conventional silencer robs an engine of 10% ‐‐ 20% of its power so that an

electronic muffler would produce cost efficiencies; especially if the exhaust heat was used to power the

electronics. This muffler application of ‘noise cancellation directly at source’ might not be perfect, but in

practical applications it is not necessary that the noise cancellation be exact; customers are often

satisfied that the approach reduces the sound level ‘significantly’ more than other approaches.

Illustration of the impact of frequency and distance on the effectiveness of noise cancellation

Direct noise cancellation at source would be a workable solution for you if your neighbors were

bothering you when they played the alpine horn. You would not hear the horn being played, but the

problem would be that your neighbours would probably not hear themselves play either! Any member


of a choir knows the importance of being able to hear yourself through monitors at a public

performance at a large theatre. Figure 2C demonstrates a possible solution when your neighbours are

just learning alpine horn breath control and playing the same note for hours at a time. Let us suppose

that the neighbour is trying to produce a note at the bottom end of the vocal range of a human base

singer, around 64 Hz, which is two octave’s below middle C on the piano. The noise control solution is to

place a mike at the player’s location, input the 64Hz tone into the SHARC and have SSSH! reproduce the

tone. A simple sound copy, slightly reduced in volume to account for distance effects, reproduced by a

loud‐speaker at a speaker 5.156 m away would be needed. This distance, half a wavelength at 64Hz,

means that the original and reproduced sounds arrive at your ears in anti‐phase and you hear nothing.

There are some practical problems with actually implementing the solution shown in Figures 2C and 2D.

Figure 2C: The second neighbors would not get the full cancellation effect. Basically the way that

the original sound volume falls off with distance is different from the way that the reproduced

sound volume from the loudspeaker falls off with distance as the two sound sources are not at

the same location. For low frequency signals, the nearest neighbour might be in the near field

of the sound source, where the sound intensity falls off at 3 db per doubling of distance, where

as the second neighbour would be in the far field, where the sound intensity falls off at 6 db for

each doubling of distance.

Figure 2D: Once the horn players have perfected playing the 64 Hz tone, they will want to jump

an octave and practice 128 Hz (one octave below middle C). Unfortunately, the loud speaker

location (5.156 m) is now exactly a wavelength away from the original sound source. This means

that the sound from the speaker is ‘in‐phase’ with the original sound; the noise cancellation

solution becomes a noise amplification problem.

The solution to the first bullet is for the second neighbors to buy their own noise cancellation system to

cancel the original sound, and also cancel the additional sounds produced by your noise cancellation

system ( ). The second bullet requires a more sophisticated solution. Now SSSH! must capture the

annoying sound and perform some frequency analysis, perhaps using the discrete Fourier transform

(DFT) implemented via the fast Fourier transform (FFT). Then a time delay, different at each frequency,

must be introduced so that each frequency component is played back at the loudspeaker in anti‐phase

to the original sound component.

Introducing a frequency‐dependent time delay sounds difficult. However, an existing piece of software

in common use in the acoustics industry would offer a solution. One third octave analysis [Nigron, 1966]

could be performed within SSSH! using a series of band‐pass filters applied to the input signals captured

by the SHARC. These filters could be implemented as finite impulse response (FIR) filter or using the

short term Fourier transform; both approaches have relative advantages and disadvantages [Vaseghi,

2008]. The output of each filter is temporarily stored in an array, and then played back after a short,

frequency dependant, delay to produce the required sound cancellation. The 450 MHZ SHARC [Analog,

2011] has been designed with this type of application in mind. As with other DSP processors on the

market, the SHARC’s computing capability comes about since its memory architecture is capable of

supporting accesses to multiple data locations at the same time, while its arithmetic units can perform


multiple floating point multiplications and additions in every cycle. Hardware floating point capability

boosts the price of a processor, but that cost is quickly recovered because it makes software

development so much easier. The principles of another algorithm for adaptive noise calculation, the

least mean squares (LMS) algorithm [Widrow and Stearns, 1985], has been demonstrated for

automobile silencers using an earlier version of the SHARC [Piper et al., 2001].

Marasco [2010] is involved in a project with junior high school students, currently at the prototype

stage, in which it is planned that the student’s playing will be recorded, with a cell phone application,

and analyzed. The main purpose of this work is to combine the sound analysis results with artificial

intelligence software to suggest ways the student could improve the sound quality of their playing, e.g.

changing the position of the jaw, tongue, diaphragm, etc. This approach could easily be extended to

improving the quality of your neighbour’s alpine horn playing; guaranteed to reduce the noise

annoyance level! A further application would be to re‐configure the Marasco analysis tool to work in

conjunction with SSSH! to generate anti‐sound so that the parents don’t hear their children’s practice

sessions ( ).

In this section, we have looked at some general issues around noise cancellation. In the next sections we

present a series of discussions about how to apply these ideas to specific noise cancellation issues; (A)

construction noise, as your neighbours build an extension to their house (hopefully including a sound‐

proof music room), (B) wind turbine noise, when you and your neighbours go green, (C) a discussion of

whether the adaptive volume control on hearing aids offers an advantage in noisy urban situations and

(D) active noise cancellation in a hospital setting such as the neonatal ward.

Cancellation of Construction Noise

It is easy to place oneself in the following situation: lying in bed, just beginning to drift into the first light

phase of sleep, when suddenly a door slams. A car alarm sounds. A nearby dog barks. While many

people can sleep comfortably through a constant sound, such the continuous hum of a refrigerator,

there is significant evidence that intermittent exposure to sudden noise events can have long‐term

detrimental effects on one’s sleep, and subsequently to their health [Somers et al., 2008]. In this section,

the issue of cancelling the following construction noise problems

Sharp noises such as hammer blows (manual or compressor driven), shouting workers, and

impact tools have been shown to slow recovery from illness or surgery [Ising, 2004],

Longer duration, sudden onset noises such as using a saw to cut a wood plank,

Echoes from surrounding structures.

The predictability of echoes from distant buildings suggests that cancellation of this problem should be

straight forward, at least in principle. For a given construction environment, SSSH! could be trained to

recognize the time between the original sound and the echo, and use that information to generate an

anti‐sound signal just when the echo is expected to arrive. The necessary analysis could possibly be an

audio variant of the algorithms used for echo cancellation in telecommunications [Malte, Schmitt, 2008].


Sharp noises from a manual or compressor‐driven hammer blow require different treatment. Here the

sound can be expected to be a large initial sound impulse followed by a smaller transient [Vaseghi,

2008]. A quick calculation allows us to calculate how fast a processor would be needed to handle an

algorithm capable of producing an anti‐noise signal in this situation. For simplicity let’s assume the

microphone used to pick up the noise source is placed a distance x m from the loud‐speakers used to

produce the anti‐sound. With sound travelling at 330 m / s then the processor must respond in around x

/ 330 s when attempting noise cancellation. If we make the microphone–to‐speaker distance around 3.3

m, this corresponds to around 10 milliseconds; more than five hundred times longer than the 20

microseconds needed by the SHARC 21469 when calculating a 1024 point fast Fourier transform to

perform one third octave analysis of the signal. However typical audio sampling systems operate with a

44 kHz sampling rate (around 20 microseconds / point) so that the physical sampling of the 1024 points

need to perform the noise‐cancellation takes around the same time (20 milliseconds) as the sound takes

to travel between the microphone and speakers.

This calculation indicates that real time calculation of a sudden onset noise cancellation signal is pushing

the audio sampling hardware rather than the processor itself. This suggests that the following approach

might work better. The first hammer blow could be recorded and SSSH! trained to use a shorter length

sound sample to recognize the arrival of the high frequency components of the next hammer blow.

SSSH! would then output a volume adjusted copy of the initial hammer blow. The initial part of longer

duration sudden sounds, e.g. saw cuts, could be handled in a similar way with the SSSH! algorithm

adjusting the later output anti‐sound to match the final part sof the saw sound using more conventional

sound cancellation techniques. A similar approach could be taken for cancelling the sounds from

shouting workers. However, while this approach might reduce the overall noise reaching the listener,

any inaccuracies in the prediction may increase the initial percussive characteristics of the sudden‐onset

noise pulse and be more of a nuisance that the original noise.

On site Wind engine (turbine) noise

Most Albertans have probably just a fleeting familiarity with wind turbine noise through articles in the

press. However in Europe, the reduced land mass means the noise created by these wind engines is very

often annoying and leads to a rejection of the plants by the local population. Factors, such as visibility of

the wind tower, rural or urban surroundings and so on play also an important role in the subjective

sensation of the wind generators [Pedersen and Waye, 2007]. However the produced noise is, of course,

the most important factor and therefore a big effort has been performed during the last years to

understand and reduce this annoying sound. In this section, a quick over view of noise cancellation

techniques for wind turbine noise is given. These ideas that can be applied to reduce the noise from the

wind farm 5 k down the road or from a local wind turbine installed when you or your neighbour decide

to go green.

There are 2 different kinds of noises from power wind engines. (A) the ‘swishing’ noise caused by the

movement of the blades which can be reduced by airfoil shape and modification of the blade edges

[Oerlemans, et al., 2008] and secondly (B) the noise from the bearings and gear which produces some

characteristic resonances of the turbine structure superimposed on a general buzzing noise


[Neugebauer et al., 2010]. This noise is caused by vibrations of metallic parts in the gearbox of the

engine and gear wheel friction. This noise is transferred through the structure and finally emitted on

surfaces of the tower or nacelle. The frequencies of this noise are in the range of 100 Hz ‐ 600 Hz

[Neugebauer et al., 2010]. If the speed of the wind generator would be always the same, the vibrations

could be reduced (or even eliminated) by changing the resonance frequencies of the systems, for

example by changing the mass of the vibrating parts. However wind generators produce much more

energy when the angular speed of the blades is dynamically changed with the wind conditions. For this

reason, the resonance frequencies are also dependent on the wind conditions and a reduction of the

vibration is much more complicated [Illgren, 2008].

A solution that has been proposed to actively reduce the vibrations' amplitude is to produce an 'anti‐

vibration' using piezo effect actuators that directly mounted on the vibrating surfaces [Illgen et al. 2008,

AC, 2007]. Fig. 3 shows a principle schematic of such an active vibration reduction system. The displace‐

ment of the vibrating mass is measured by a sensor. Principally, different types of sensors can be used

(force, speed, acceleration and path or a combination of these). A regulation circuit based around a

digital signal processor is used to calculate the counter force that will drastically reduce the amplitude of

the vibrating mass. The highest frequency that can be depressed is dependent on the overall bandwidth

of the systems as well as on the inertia of the actor‐mass‐system. In wind generators, frequencies

between 100 and 600 Hz are the most annoying. These relatively low frequencies may easily be

detected and reduced by today available systems [Neugebauer et al., 2010; Illigen et al., 2007]. In

practice, a real existing vibrating surface will have multiple modes requiring more complex set‐ups of

the actuators; so that it's not possible to completely reduce the noise. Another principle is to accelerate

another mass (quench mass) in a way that its movement is inversely phased to the vibrating mass. The

emitted noise level will be lower as the resulting amplitude of the system will be strongly reduced. Using

either technique would leave to a lowering of the intensity of main resonance frequencies by some dB

[Neugebauer et al., 2010]; reducing the annoyance spread through the surrounding environment.

A community might use the following arguments to persuade a wind engine owner to implement these

noise reducing techniques: (A) The signal used to regulate the active compensation is a very good

indication of changes in the vibration performance. Monitoring the system to identify large changes in

the vibratory behavior would provide early indication of possible future damage occurring to the system

and (B) the wear of the system is lower if the amplitude of vibration is reduced. Fissure propagation are

dependent of the vibration amplitude which would reduced by these active systems.

Vibrating mass

SForce, acceleration, speed or path sensor

A Phase detector and regulation circuit

Actuator

Fig. 3: Principle schematic of the active vibration reduction


Offsite Wind engine (turbine) noise cancellation and the Ranchlands Hum

A different set of analysis algorithms are needed when the nuisance sound generator is ‘far in the

distance’ – e.g. residual noise from a wind turbine or the Ranchlands ‘Hum’. In this situation, it would

seem reasonable to assume that the air‐transmitted sound would arrive as a plane wave (Fig. 4A). In

principle, having the DSP processor determine the relative arrival times of the nuisance sound at an each

microphone in an array of microphones would provide SSSH! with the volume and direction of arrival of

the plane wave. In actual fact, if there was a series of SSSH!s spread around a neighbourhood, the

multiple bearings could be used for triangulation so that the nuisance sound could be located. For this to

work, the microphone array must be spread across ‘a decent length’; otherwise the array would act like

one large microphone. If this happens, then the phase information would become more difficult to

determine.

With the intensity and bearing of the noise source determined, a cancelling anti‐plane wave can be

generated; but now the practical problems start. Changes across a room of the sound intensity of a far‐

away nuisance noise would be expected to be minimal since the dimensions of a house are small

relative to the distance to the sound annoyance. However the sound from a local loudspeaker rapidly

changes with distance, making its spherical sounds waves relatively ineffective in cancelling the plane

wave noise source. These problems might be overcome by using an array of speakers. As with the

microphone array, the speaker array must be spread across a ‘fair distance’, otherwise the multiple

speakers effectively act as a single large speaker so that the plane wave can’t be generated. The ‘quality’

of the anti‐plane wave generation would be best along a line perpendicular to the centre of the array,

and the ‘area’ over which that quality is good increases with the length of the speaker array.

Figure 4: (A) Information, intensity and direction, about the plane wave characteristics of a distant sound can be

captured by an array of microphones, with the anti‐sound generated, in principle, by an array of loud‐speakers. (B)

Placing the array of speakers inside a room introduce multiple reflections from the wall surfaces which can be

treated mathematically as additional arrays of virtual sound sources.


Note that the use of ‘area’ is a misnomer since we are dealing with 3D volumes. Hopefully this does not

mean that we need a two dimensional array of speakers. This observation is probably true, given the

fact we want noise cancellation in only a small volume about the size of our head and loud‐speakers are

physically large and are not point sources of sound. In addition, since the speakers will probably be

employed in a room rather than in the weather outside the house, the reflections of the sound from the

walls would create a 2D / 3D array of virtual speakers (Figure 4B). It will require a few experiments to

determine whether this virtual array is a help or a hindrance.

The definition of what constitutes a ‘decent length’ for the microphone and speaker arrays also needs to

be determined. Let’s consider an array of 5 speakers with the ‘sweet spot’ of noise cancellation along a

line perpendicular to the centre speaker. In order to not act like a single large speaker or a dipole or

quadrupole sound sources, all or which generate far‐field spherical waves [Drussel, 2011], the speakers

would need to be separated by distances of the order of a half‐wavelength or more. Suppose we

proposed to use this approach to nullify the Ranchlands Hum [CTV, 2010; Ranchlands, 2011] which

appears to occur in the 40 Hz to 45 Hz range [Patching Associates, private communication]. It would

require a five speaker array that is 20 m long. However as this approach would require a speaker array

of under 2 m in length at 600 Hz – 1000 Hz, it might be feasible for the nuisance noise from a distance

wind turbine, which sounds like an annoying mosquito [Illgen, 2008].

A possible approach to avoid using arrays of speakers is shown in Figure 5 with a series of speakers using

information from an external calibration microphone and an internal microphone to allow SSSH! to

generate the correct phasing and intensity for a special local configuration of speakers with

precalculated signal phasing. The analysis becomes more complicated, but the approach seems feasible

as it is analogous with how home theatre systems are corrected for time delays associated with the

Figure 5. The biggest problem to overcome in having an in‐house noise cancellation system is the

external ‘distant’ noise source will probably have far‐field characteristics (quasi‐plane wave) for the

listener while the loudspeakers producing the anti‐sound signals will have near‐field characteristics

(quasi‐spherical waves). Further complicating the analysis, especially for low frequency noise

cancellation, is the ability for noise source and speakers to independently set up a series of room

resonances. For a discussion of this effect see Drussel [2011]. Having the speakers directly attached to

the listener, i.e. using hearing aids, might overcome these problems.


different distances of each of the (5 or 7) speakers to the listener. The home amplifier sends a (white

noise) sound pattern to each speaker in turn. This information is picked up by a single microphone and

the necessary time delay compensation is automatically calculated for an improved listening experience

over a small volume, a couch or group of chairs for example.

What does this mean for our wind generator problem? Since the wind turbines are really far away from

the position of the listener, the assumption that the noise arrives as plane wave is surely true. So the

basic idea of using an array of speakers to cancel the noise should be applicable. In addition, a second

SSSH! device could be set at the wind turbine site to provide ‘at source noise cancellation” to support

the anti‐vibration devices. The question the authors ask ourselves is “if the approach is so simple, why

has it not been done before”. We suggest a couple of possible reasons: (i) There are so many problems

to solve with on‐site turbine noise that nobody has yet begun to consider home cancellation issues and

(ii) its more effective to cancel noise onsite since the cost of providing one noise cancellation system for

each home affected by a wind turbine noise is prohibitive. In response to the second comment,

communities put up a wind break that is collectively useful, so why not a noise cancellation forest of

speakers somewhere between the turbines and the community? After all, the phasing of the sound from

a long community‐owned array of speakers used to cancel out a plane wave more effectively than a

large number of individual small arrays, and the anti‐noise plane wave direction could be steered to

compensate for changing environmental conditions; temperature or wind shears.

Hearing Aids – The ultimate noise‐cancelling device?

It might be argued that many senior citizens may already make use a device much more capable of

reducing construction and other noise pollutants than the proposed SSSH! approach. In this section, we

discuss the basis behind adaptive volume control in hearing aids and ask whether the wearers of hearing

aids have an advantage over the rest of us in urban noise environments.

Digital hearing aids nowadays are very complex, highly integrated devices which, in a few cubic

centimeters, package one or two microphones, an ASIC (application specific integrated circuit) including

an analogue front end, a digital signal processor (DSP) and typically a Class D power amplifier which

drives a small speaker. The whole system is powered by a Zinc‐Air battery which doesn't allow for huge

power consumptions. The basic principle of a hearing aid is to amplify incoming audio signals in a

manner that compensates for (high frequency) hearing loss related to aging or accident. However, most

of today’s hearing aids contain some more or less intelligent algorithms to adapt the gain to the

environment [Hawkins et al, 2003]. For example, the aid can be programmed so that the full gain is

available for small sounds, but is limited for sudden arriving loud tones (e.g. hammer blows or

somebody shouting behind the listener. Noise cancellation / reduction can be achieved for aids with (A)

an omni‐directional microphone placed at the back of an aid to take in the background noise and (B) a

directional one which is focused on voice of the dialog partner, but still picks up some background noise.


The voice‐to‐noise ratio of the aid’s output to the ear can be boosted by the application of some simple

digital signal processors.

The problem is that the hearing aid is very limited in terms of available processor capability and power

requirements. To make use of the hearing aid to solve urban noise problems for the ‘average’ listener,

we suggest two approaches might be considered – (A) a passive approach that might be appropriate for

solving low frequency noise issues such as the Ranchlands Hum, and (B) active approaches for higher

frequency wind turbine noise and construction noise.

Normally a hearing aid uses a small speaker in the ear for the transmission of amplified high frequencies,

with some low frequency boosting to compensate for the blocking effect of the speaker and the mounts

that hold the speaker in the ear channel. The problem in a noisy environment is that noise leaks around

the speaker and can nullify the advantages of an aid. To solve this, and other problems, an ear mold that

both supports the speaker and blocks all external noise are available. We are suggesting an investigation

of a variant of this approach to reduce the impact of the 40 – 48 Hz Ranchlands Hum while the source of

the Hum is being investigated. While the ear mold totally blocks the low frequencies, the hearing aid is

used to transmit normal day‐to‐day sounds to the listener, essentially acting as an all‐pass, zero gain,

amplifier for all frequencies above say 100 Hz. This is a far more effective approach that simply using

totally passive ear‐plugs or ear mufflers, which would block both the Hum and all normal speech sounds

needed for everyday life.

The problem with wind turbine noise or construction noise is that these are in the normal speech range

so the approach of using an ear mold block plus an all‐pass amplifier hearing aid will not work. Many

modern hearing aids come with Bluetooth connectivity to TVs or cell phones / land lines. Could this

connectivity be used by SSSH! to provide a noise cancelling signal directly to the hearing aid avoiding all

the problems of speaker configuration shown in Figure 5?. As discussed earlier, the effectiveness of

noise cancellation is position dependent so the hearing aid wearer would have to move no more than

about 1/4 of the wavelength of the noise annoyance. For wind turbine noise in the 600 Hz to 1000 Hz

range, this distance corresponds to less than 10 cm, probably smaller than the changes in a person’s

head position on a pillow at night. Note however, we are not looking for a perfect solution, just an

improved sound environment, so further investigation is needed.

Improving this approach means that SSSH! would need to be combined with a monitoring device, e.g.

web camera to monitor the wearer’s position, with SSSH! doing video analysis and continually updating

the cancellation signals. Perhaps a better approach would be to physically move the SSSH! algorithms

directly into the hearing aid’s DSP system as shown in Figure 6. Here the twin external speakers of the

aid are used to monitor the oncoming sound from multiple directions with another microphone

mounted in the ear canal providing an error signal. This approach would use both a noise reduction

system and active noise control (ANC) to create a quiet zone directly at the membrane of the ear‐drum

(Figure 6). Doclo et al, [2007] and Serizel et al, [2010] have indicated that such a simple system will not

work as the delay in the sound (and noise) introduced by the noise reduction system confuses the ANC


software, necessitating an investigation of how the noise‐reduction and ANC can be integrated. An

important question to consider is whether the hearing aid DSP chip has the power to handle this

situation, both in terms of battery power and computational power. One solution might be to use a bi‐

directional Bluetooth connection to off‐load the audio signals captured by the aid to an external SSSH!

system for calculation of the anti‐noise sound solution, which is then transmitted back to the aid.

However, although this might avoid increasing the size of the hearing aid to permit inclusion of a more

computationally powerful processor, it is unclear whether the power needed for a Bluetooth

transmitter would drain the battery power any slower than if the hearing aid DSP chip did all the

calculations.

Conclusion

The authors started this article with the intention to examine the practicality of using a home theatre

system, and some student experiences with the Analog Devices SHARC DSP processor, to cancel a noise

annoyance in one Calgary community, the Ranchlands Hum. We went on to examine a wider variety of

noise pollution issues, and come up with an unexpected conclusion. It would appear that standard

hearing aid equipped with an ear mold and used as a zero gain, all‐pass amplifier offers a possible short‐

term solution to removing low frequency noise annoyances while attempts are made to identify the

noise source and apply corrective measures.

Short term investigations of noise related algorithms are very appropriate for summer or term projects

for engineering students. For example, SSSH! could be re‐configured to form a simple tool to hunt down

quiet spots from the Ranchlands Hum in a home. The same frequency analysis that would allow SSSH! to

power a series of speakers to attempt to produce the anti‐noise signals could be used to activate a

series of LEDs. This would provide a basic tool could be used by a home owner in their investigations on

the Hum. Firms interested in putting in a couple of hours per month mentoring such students in acoustic

projects to avoid ‘errors that are obvious once somebody shows you that they are obvious’ should

contact Mike Smith (smithmr @ ucalgary.ca). After all, the industrial saying “Experience is what you get

just after you needed it the most” translates into an academic environment as “Experience is earned, not

learned”.

Noise reduction system

Active Noise Control

tympanic membrane

ear-canal

ANC-Feedback signal

Noise leaking

Figure 6: Principle of cascading Noise Reduction and Active Noise Control


Acknowledgements

Financial support provided through an industrial collaborative research and development grant from

Analog Devices, CDL Systems and Natural Sciences and Engineering Research Council (NSERC) of Canada:

CRD‐365295. Additional support provided by the University of Calgary, Canada. The SHARC evaluation

boards are made available through Analog Devices University Donation program. M. Smith has been

Analog Devices University Ambassador in Canada since 2004. Thanks to Richard Patching (Patching

Associates, Calgary) and Casey Fisher and Debbie (APEX Hearing Systems, Calgary) for useful discussions.

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