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Changing with the Times: Additional Criteria for a Good Feedback Cancellation Algorithm

The use of active feedback cancella-tion algorithms in commercialhearing aids is a relatively recent

development.1 Today, almost every man-ufacturer of digital hearing aids use someform of an active feedback cancellationalgorithm in their digital hearing aidproducts. Unfortunately, what “active”means and how effective is the “cancel-lation” varies dramatically among manu-facturers. Making matters worse, thereare no standardized protocols for theevaluation of active feedback cancella-tion algorithms. The most commonlyreported criterion is the increase inAvailable Gain Before Feedback (AGBF).

But AGBF is only one aspect of theperformance of an anti-feedback algo-rithm. An algorithm with a high AGBFmay be inadequate when it is evaluatedusing a different criterion (eg, artifacts ornaturalness of sounds, responsiveness toprovoked feedback). Understandably,the lack of a clear criterion makes itextremely difficult for dispensing profes-sionals to select the most effective anti-feedback algorithm for their patients.

This article reviews the factors thatmay limit the performance of a feedbackcancellation system. We will explainhow the new Inteo hearing aid address-es these issues in its multi-directionalactive feedback cancellation algorithm,and report on some of its effectivenessdata while using different criteria.

How do Active FeedbackCancellation Systems Work?

Kuk et al1,2 reviewed the basics ofactive feedback cancellation algorithmsand indicated the practical limitations

prevents feedback by either adaptivelymanaging the gain of the hearing aid sothat it is lower than the damping offered bythe acoustic path, or by canceling the feed-back sound before it becomes audible.

In order to determine if the sounds atthe hearing aid microphone originate pri-marily from the hearing aid itself (eg,leaked sounds through the feedback path),an active feedback system has to performon-going correlation analyses between theinput sounds to the hearing aid (whichinclude both direct environmental soundsand leaked sounds from the hearing aid)and the output sounds from the hearingaid. A high correlation suggests similarityof the two sounds. This is the case if theinput sounds to the hearing aid originatemostly from the hearing aid itself—an indi-cation of a feedback signal. A low correla-tion means the two sounds are dissimilar.This is the case when the input soundsoriginate primarily from the wearer’s envi-ronments and are not feedback. The twolines indicated as A and B in Figure 1 showthe paths where the correlation is made.

Two Strategies for Managing Feedback

Managing audible feedback byreducing hearing aid gain. One way ofactively managing audible feedback(whistling) is to immediately limit orreduce the gain of the hearing aid whenaudible feedback is detected so it is belowthe feedback gain limit. Gain reduction ismaintained until the condition creating theaudible feedback disappears. This is shownin Figure 2 where the insertion gain at3,500 Hz is temporarily reduced below thefeedback gain limit. One may also opt touse a notch filter of a specific frequencywidth to filter out the feedback frequency.

The advantage of active gain limitationis its responsiveness in managing audiblefeedback whistling. This is beneficial in sit-

of such systems. Briefly, all active (asopposed to passive systems such as fixedgain reduction) feedback management sys-tems have to 1) Identify the feedbacksounds reaching the hearing aid micro-phone, then 2) Resolve the feedback issueusing one of two approaches (or both).

Identification of FeedbackIn an ideal world, sounds reaching a

hearing aid microphone would only com-prise of direct environmental sounds thatneed to be amplified. Unfortunately, in thereal world, sounds reaching the hearing aidmicrophone include both the direct envi-ronmental sounds as well as sounds that“leaked through” the hearing aid.

The acousticpath in which theleakage occurs iscalled the feedbackpath. The feedbackpath determines thefrequency andphase characteris-tics of the feedbacksound. The amountof sound leakage (orfeedback) dependson the dampingprovided by the earmold/hearing aid shell,etc. If the amount of damping in theacoustic path is smaller than the amount ofamplification offered by the hearing aid,more sounds will leak through the hearingaid and be picked up at the microphoneopening. This leaked sound or feedback isinaudible to the wearer and is present in allhearing aid use.

However, if this sound is amplified overand over again, its magnitude may become

so large that itexceeds the damp-ing and becomesaudible as the typ-ical feedbackwhistling. Anactive feedbackcanceling system

This article was submitted to HR by Francis K. Kuk, PhD, director of audiolo-gy, at the Widex Office of Research in Clinical Amplification (ORCA), in Lisle,Ill; Anders H. Jessen, BScEE, and Kristian T. Klinkby, MSEE, research engineers,and Lise B. Henningsen, MA, research audiologist, at Widex A/S, Vaerloese,Denmark; and Heidi Peeters, MA, and Denise Keenan, MA, research audiolo-gists, at Widex ORCA. Correspondence can be addressed to Francis Kuk,Widex ORCA, 2300 Cabot Dr, Ste 415, Lisle, IL 60532; e-mail: fkuk@aol.com.

techtOPIC

FIGURE 1. Schematic showing the mechanism of active feedback manage-ment through feedback cancellation in a single path. The “feedback estima-tor” correlates the sounds going into the hearing aid amplifier (B) and thesounds leaving the hearing aid amplifier (A) to identify feedback.

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Criter ia for a Good Feedback Algorithm

uations where there is a sudden change inthe acoustic conditions disturbing thefeedback path (eg, wearing a hat or movinga telephone handset to the ear). Theapproach provides fast relief from feedbackwhistling. Because gain reduction at specif-ic channels (or using notch filters with aspecific width) may potentially compro-mise audibility (and intelligibility) duringthe time when this mechanism is “active,”systems with a larger number of processingchannels may be less compromised thanthose with fewer independent channels ornotch filters with broader bandwidths.

Managing inaudible feedback byremoving the feedback sound. Thesecond approach to managing feedback isto remove it from the input before it is evenaudible to the wearer. This is achievedthrough the feedback estimator (FE) shownin Figure 1. After feedback is identified, theFE block generates a new sound (designat-ed as “C” in Figure 1) that can be subtract-ed (or cancelled) from the input sound.For complete cancellation, Sound Cshould be equal to the feedback sound.Because any changes to the feedback pathchanges the feedback sound, the FE blockis continuously active in order to have anupdated estimate of the feedback sound.

The advantage of the cancellationapproach is that it prevents the feedbacksound from becoming audible by continu-ously accounting for any changes in thefeedback paths. This allows for more AGBFthan hearing aids not using such anapproach. On the other hand, becausemany commercial algorithms using thisapproach are designed to respond to grad-ual changes in the feedback path, they maynot be responsive to sudden changes in thefeedback paths.

In summary, the two common approach-es to actively manage feedback differ in theirresponsiveness as well as the potentialchanges in the available gain. Limiting thehearing aid gain is a quick and effective wayto managing audible feedback—but avail-able gain may be sacrificed. On the otherhand, canceling the feedback sound beforeit becomes audible is an effective way to pre-vent the occurrence of feedback and allowthe wearers to use more gain—but this algo-rithm alone may not be responsive to sud-den changes in feedback paths.

The Rest of the Story: AccuratelyEstimating the Feedback Path

The effectiveness of an active feedbackcancellation system depends on the crite-ria one uses for its evaluation. In an ideal

condition have a high likelihood of esti-mating the actual feedback sound.

In contrast, when the gain of the hear-ing aid is low, or when the damping in thefeedback path is high, the magnitude of thefeedback sound will be small when com-pared to the direct sounds from the envi-ronment. Thus, the correlation analysiswill have more difficulties estimating theexact feedback sound. Consequently,incomplete or no cancellation may occur.

Under-modeling: The trade-offbetween complexity and accuracy. Inorder to completely cancel the feedbacksound, one needs to generate the samesound with a very high degree of precision.

This is not feasible even in state-of-the-art professional audio recordingsystems, let alone a hearing aid thathas to operate at a limited currentdrain. Thus, in real-life, hearing aidswith an active feedback cancellationalgorithm are designed with somedegrees of under-modeling (eg, theestimation is not exactly the same asthe true feedback signal) in order tobe practical. They differ by howmuch they under-model.

In some conditions, under-mod-eling of the feedback path may beminimized with a careful design.Typically, it is improved by increas-ing the precision and complexity ofthe estimation process, with agreater improvement in AGBF froma more precise or accurate modeling

process. Unfortunately, this will increasethe current drain on the hearing aid,increase the size of the chip, or both.

Mal-adaptation: Distinguishing“right” from “wrong.” A key characteris-tic of an active feedback cancellation sys-tem is that its operation is dependent on theresults of the ongoing correlation analysisbetween the input and the output. Thisassumes that sounds leaking from the hear-ing aids are the only ones with a high cor-relation. Unfortunately, there are sounds inreal-life that would also yield a high corre-lation under such an analysis. For example,sustained sinusoids, long whistles, or somemusical notes that do not change over timecould also yield a high correlation. Thiscould mislead the system to identify theinput sounds as feedback when there is nofeedback. A poorer sound quality resultswhen the mis-identified “feedback sounds”are cancelled from the input.

Another example of a mal-adaptation arti-facts is the “phantom sound” generation. Thisis shown in Figure 3a-c with a state-of-the-art

situation where the feedback path doesnot change, active feedback algorithmscan be designed to yield a very high AGBF.In real-life, feedback paths may changeunpredictably. Thus, the feedback soundsalso change.

An anti-feedback algorithm that yields ahigh AGBF in one static test condition maynot be quick enough to respond to changesin feedback paths to stop audible feedbackwhistling. Sometimes, “artifacts” and poor-er sound quality may occur as a conse-quence of the anti-feedback algorithm. It isannoying to the wearers if the hearing aidhas many artifacts, or if it does not stopwhistling (such as when wearing a hat or

putting the telephone close to the ear) eventhough the device may yield a high AGBF.

The following are some reasons whytwo active feedback cancellation algorithmsthat report the same increase in AGBF maybe different from each other in real life. Toa certain degree, these are important con-siderations when designing an active feed-back cancellation algorithm that yields anoptimal balance between achieving a highAGBF and freedom from “artifacts.”

How the system handles “uncer-tainty.” The estimation of the feedbacksound can be perfect if the sounds goinginto the hearing aid are the feedbacksounds only. In real-life, the sounds goinginto the hearing aid are a combination ofdirect sounds and the feedback sounds.

When the gain in the hearing aid ishigh, or when the damping of the feedbacksound going through the feedback path islow (eg, a lot of leakage), the majority ofthe sound at the hearing aid input will bethe feedback sound. The results of the cor-relation analysis performed under such a

FIGURE 2. Gain limitation as a means to manage audiblefeedback. In this case, the red curve shows the AGBF ofthe hearing aid and the blue curve shows the desiredgain. Desired gain at 3,500 Hz will be reduced becausethe AGBF is lower than the desired gain.

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DSP hearing aid in an active feedback cancel-lation mode. The top half of the figure showsthe in-situ output waveform of the hearing aidto a continuous 3,000 Hz sinusoid. The lowerhalf of the graph is the spectral analysis takenat different times. Figure 3a was taken afterthe sinusoid has been presented for 29 s. Aspectral analysis of the output showed theoriginal signal at 3,000 Hz, along with a small-er feedback signal at around 2,400 Hz. Whenthe section was measured at 33 s (Figure 3b),the magnitude of the feedback signal hasincreased to be the same as the 3,000 Hz sig-nal. That is perceptible as an audible feed-back. Figure 3c is the measurement taken 5 safter the termination of the 3,000 Hz input (orat 45 s interval). A spectral analysis showedthat this signal was centered at 2,400 Hz. Thiswas the anti-feedback signal that was stillbeing generated to cancel the feedback sound.These observations are not predictable basedon the information from AGBF increase.

Rate of tracking: Responsiveness tochanges. The changing nature of real-lifesituations means the feedback path changescontinuously and sometimes unpredictably.The changes may be gradual like the “slip-ping out” of the hearing aid as the day goeson. Sometimes the changes are more abruptlike when a hat is worn or when the wear-er hugs another person. In those cases, afixed rate of estimation of the feedback pathmay not be responsive enough to the sud-den changes in the acoustic conditionseven though the feedback model improvesthe AGBF significantly under the ideal situ-ation. Thus, an active feedback cancellationalgorithm must be able to consider theseissues in its design—fast rate of adaptationwhen it needs to be fast, and slow when itcan afford to be slow.

Interactions with other algorithms.It is customary to report on the efficacy of a

Summary of current situations. Theeffectiveness of a feedback cancellationalgorithm is a reflection of the goodness ofthe feedback estimation model and howthe model manages the changing charac-teristics of the feedback path. In thisprocess, the complexity of the chip, as wellas the considerations paid by its designersto minimize real-world artifacts, woulddictate its effectiveness.

A large gain improvement (AGBF) isdesirable. However, it is not sufficient forwide application of the algorithm and itseffective use in real-life situations.Minimizing the uncertainty of the estima-tion and any mal-adaptation artifacts,increasing the responsiveness of the sys-tem, and integrating the algorithm withother processing algorithms within thehearing aid are means that go beyond sim-ply improving the AGBF of an active feed-back cancellation algorithm.

How Multi-Directional ActiveFeedback Cancellation Works

Recently, Widex introduced Inteo, thefirst digital hearing aid that uses IntegratedSignal Processing (ISP) as its technologyplatform. ISP uses a coordinated and con-certed approach to signal processing so thatwearer and environmental information,along with the intermediate results of eachprocessing unit, are shared among other sig-nal processing units in order to achieve thebest sound quality and intelligibility with-out artifacts in most, if not all situations.

In the Inteo, the various processing fea-tures are grouped into three functional mod-ules: 1) The high definition sound analysis(HDSA) module characterizes and classifiesthe nature of the acoustic environments; 2)The high definition sound processing (HDSP)module includes all the processing functions,such as compression, noise reduction, feed-back cancellation, etc, and 3) The high defini-tion system optimizer (HDSO) moduleensures optimal and efficient operation of allthe components and processes within theInteo hearing aid.

A Dynamic Integrator (DI) coordinatesthe activities within these three modulesand references them to the wearer charac-teristics so that the output of the Inteomeets the needs of the wearers and their lis-tening conditions (Figure 4). What followsis a description of how the active feedbackcancellation algorithm is implemented inthe context of the ISP platform.

The spatial feedback tracer (SFT) of theHDSA module analyzes the sound environ-ment to determine the presence of feedback

feedback cancellationalgorithm when thehearing aid is in a lin-ear omnidirectionalmicrophone mode.The implicit assump-tion is that other pro-cessing features onthe hearing aid do notinteract with theeffectiveness of theactive feedback can-cellation algorithm sothat the observationsmay be generalized toreal-life use of thehearing aids.

Unfortunate ly,such an assumption is rarely true today.Nowadays, hearing aids that use an activefeedback cancellation algorithm almostalways use different degrees of complex non-linear processing and various nonlinear fea-tures. For example, the use of wide dynamicrange compression (WDRC) and noisereduction on today’s hearing aids would sug-gest predictable but non-uniform gainchanges on the hearing aids, depending onthe nature and level of the acoustic inputs inthe listening environments. As indicatedbefore, these gain fluctuations could lead toincreased uncertainty in the estimation ofthe feedback sound, and consequently errorsin the cancellation.

The use of an adaptive directional micro-phone system would also suggest the possi-bility that the changing polar pattern in real-life situations may alter the feedback pathcharacteristics. This means that the feed-back cancellation system must be respon-sive to the changes in the feedback pathsconsequent to these changing polar pat-terns. In a similar vein, any audible feed-back could alter the polar pattern of theadaptive directional microphone and dimin-ish its effectiveness and/or create artifacts,such as loudness changes or hissing sounds.

These potential interactions have severalimplications. One is that the AGBF informa-tion is “limited” in that it does not reflect thepotential interactions among the processingalgorithms in real life. Secondly, the infor-mation reported on the AGBF will be sub-stantially different in real life. Thirdly, audi-ble feedback and feedback artifacts mayoccur in real life under the “right” condi-tions if adequate precautions are not or can-not be taken. Consequently, integrationamong the different algorithms of the hear-ing aid is critical if one were to realize theintended effectiveness of each algorithm.

FIGURE 3. Waveform and spectra illustrating an example of mal-adapta-tion artifact. The top figure is the waveform of the in-situ output. Sectionsof the waveform were taken at various times to study their spectral con-tent (shown on bottom half of figure).

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Criter ia for a Good Feedback Algorithm

by comparing the input levels measured at the dual microphones.Concurrently, input signals from each microphone are analyzedthrough correlation with the output to further identify the charac-teristics of the input sounds. A high correlation between the inputand the output suggests a feedback sound, but only if the conclu-sion agrees with the analysis performed at the dual microphones(that would support the feedback nature of the input sounds). Ahighly correlated sound that is estimated to be non-feedback at thedual microphones raises doubt on the feedback identity of thesound because continuous stationary signals (such as sinusoids,whistling, or musical notes) may also have a high correlation.Thus, the SFT, by predicting the identity of the input at the micro-phone openings and integrating the results with the traditional cor-relation analysis, determines more accurately the presence of feed-back sounds and reduces mal-adaptation artifacts.

The Inteo also optimizes the effectiveness of the SFT by using apatented method that adaptively changes its rate of estimation ofthe feedback sound. This is possible because in some situations,such as high gain and quiet environments, the relative level of thefeedback signal is higher and is clearly identifiable. Thus, the rateof estimation can be short. Other more difficult situations (eg,music, whistles, etc) which have a high correlation between theinput and the output could be easily confused with feedback, andthus a longer period or a slower rate of estimation is needed.

In order to minimize the impact of the adaptive directionalmicrophone on the feedback path estimation (and vice versa),maximum gain assignment and feedback path estimation is donebetween the extreme polar patterns formed by the dual micro-phones. This means that the accuracy of the feedback estimationwill not be affected by the adaptive directional microphone. By thesame token, any audible feedback, if present, will not affect thepolarity of the adaptive directional microphone. This increases theaccuracy and stability of the feedback estimation and decreases anytracking errors that may arise from the adaptive directional micro-phone. From the wearers’ standpoint, this ensures the successfuluse of both the adaptive directional microphone system and theactive feedback cancellation system in more real-life situations. TheAGBF is improved.

As discussed, the accuracy of modeling a feedback path (oridentifying the feedback sound) is dependent on the complexity ofthe model. A more complex model yields a more accurate estima-tion and results in a higher AGBF. Unfortunately, this requires morecurrent drain and chip complexity. To achieve a higher accuracy,the HDSO within the Inteo uses a state-of-the-art chip that is manytimes more complex than that used in the Diva. Furthermore,using EcoTech II technology (which is a collection of approachesto maximize current efficiency), the Inteo drains at lower than 1.0mA even with all its features activated. In the CIC version, the cur-

rent drain is a mere 0.6 mA!The identity of a specific sound as a potential feedback sound

or an audible feedback sound is conveyed to the DynamicIntegrator (DI). This unit integrates the feedback information withother information and informs the multi-directional active feedbackcancellation (MDAFC) unit within the HDSP module so it can gen-erate the appropriate feedback management strategy. In the Inteo,two ongoing strategies are used in a complementary manner. Foraudible feedback that results from a sudden change in the feedbackpath, immediate gain limitation is used. For gradual changes in thefeedback path, feedback sound cancellation is used to cancel thefeedback sound before it is even audible. The AGBF is alsoincreased as a consequence.

Other signal processing algorithms within the HDSP module alsocontribute to the DI. For example, information from the compres-sion circuit is conveyed to the DI so it can ensure that gain fluctua-tions in the estimation are below that of the compressor. Thisimproves the stability of the estimation and limits the amount ofuncertainty errors.

Effectiveness of the SystemThe integration of the active feedback canceling system into

other processing units of the Inteo hearing aid is an example of theapplication of ISP. When looking at the following data, one shouldbe aware that the data reported here were obtained with the Inteo inits normal operating mode. This means that other adaptive algo-rithms, such as the speech intelligibility index (SII) optimizationalgorithm,3 the 15-channel fully adaptive directional microphonesystem, and the multi-segment compression system with a low com-pression threshold are active during the measurements. The impli-cation is that the reported results reflect more closely the real-life

FIGURE 4. Block diagram showing the interconnection between blocks (ormodules) within the Inteo ISP platform for the multi-directional activefeedback canceling algorithm.

FIGURE 5. Increase in max AGBF between the "Feedback On" and"Feedback Off" conditions. An open earmold was used.

FIGURE 6. Frequency of occurrence of feedback (and no feedback) when atelephone handset is placed on the ear/hearing aid. In all cases of feed-back, the whistling stopped within fractions of a second.

SEPTEMBER 2006 hearingreview.com 45

use of the system.Increase in maximum available gain before feedback.

To measure the potential increase in AGBF, the maximum gain ofthe Inteo IN-9 hearing aid (set for a flat 80 dB hearing loss andfit with a free-field earmold) in the “Feedback On” and“Feedback Off” conditions were examined on the SoundTrackerin an IAC sound-booth.

Figure 5 shows the individual increases in AGBF. There is awide range of AGBF increase between 2,000-4,000 Hz from as lit-tle as 8 dB to as much as 19 dB. An average of 12-13 dB was notedfor the group. No increase in AGBF was noted below 1,000 Hz,possibly because feedback usually occurs above 1,000 Hz and tar-get gain is typically reached below 1,000 Hz (thus, no need forgain increase). Nonetheless, the increase in AGBF allows themajority of wearers to achieve their desired gain without feedbackin many more listening situations. The ability to use the targetgain more consistently could result in better speech intelligibility,better sound quality, and a hassle-free listening experience. Inaddition, an even larger vent diameter may be used successfullyto reduce the occlusion effect (if any) that has a shell origin. Theadditional gain increase will also result in a “purer” sound byallowing a higher feedback margin.

No or minimal audible feedback when provoked. Theincrease in available gain before feedback addresses the long-termbenefit but not the short-term stability of the active feedback sys-tem. To assess the performance of the active feedback system tosudden changes of the feedback path, we placed a telephone hand-set to the subjects’ ear and noted if feedback was audible. In cases

where feedback occurred, we also noted the time it took for theaudible feedback to stop. The data from 17 subjects (or 34 ears)wearing the hearing aids (8 wore open-fitting élans and 9 woreITCs and BTEs with 1-3 mm vent diameters) is shown in Figure 6.Feedback whistling was absent in 28 of the 34 ears (84%) evenwhen the handset was placed directly on top of the hearing aid andthe pinna. Almost half of the cases were open-fitting élans. In caseswhere feedback was noted, it stopped within fractions of a second(estimated to be within 0.2 s). This suggests that the active feed-back algorithm is responsive to sudden changes in the feedbackpath. This ensures that its wearers can more consistently use thehearing aids in more diverse listening situations.

Freedom from mal-adaptation. As discussed, mal-adaptationartifacts include situations where the feedback algorithm mistakenlycancels the “non-feedback” direct signals (eg, whistling, music) orgenerates a feedback cancellation signal after the termination of thedirect sound. In the former case, one may ask the wearers to rate thesound quality of music between feedback “on” and “off.” Thereshould not be any quality difference between the two conditions ifthere are no mal-adaptation artifacts. In the second case of generatinga signal (if that occurs), one should be able to measure the in-situ out-put of a hearing aid and examine the in-situ response after the termi-nation of the “feedback-like” stimulus.

Figure 7 displays the real-ear recordings of the in-situ outputof the Inteo (with active feedback cancellation “on”) with anopen earmold during and after the presentation of a 3,000 Hzsinusoid. In contrast to Figure 3, which shows the presence of themal-adaptation artifact, Figure 7 shows no trace of any feedbacksignal during the presentation of the input or of any feedbackcancellation signal after the presentation of the input. Theseobservations suggest that mal-adaptation is absent with the activefeedback algorithm.

In summary, these data indicate that the Inteo multi-directionalactive feedback cancellation algorithm is effective in increasing theAGBF by as much as 15-18 dB, while at the same time beingresponsive to sudden changes in the feedback paths without creat-ing artifacts under real-world conditions. w

References1. Kuk F, Ludvigsen C, Kaulberg T. Understanding feedback and digital feedback

cancellation strategies. The Hearing Review. 2002;9(2):36-43.

2. Kuk F, Ludvigsen C.The real-world benefits and limitations of active digital

feedback cancellation. The Hearing Review. 2002;9(4):64-68.

3. Kuk F, Paludan-Muller. Noise management algorithm may improve

speech intelligibility in noise. Hear Jour. 2006;59(4):62-65.

FIGURE 7. Waveform and spectrum of the in-situ output of the Inteo hear-ing aid in active feedback cancellation mode. Sections of the waveformwere taken during the stimulus (3,000 Hz) presentation and after the stim-ulus presentation to illustrate the lack of mal-adaptation artifacts.

Reprinted with permission. “Changing with the Times: Additional Criteria for a Good Feedback Cancellation Algorithm”,

Hearing Review, September 2006; Volume 13, Number 10: Pages 38, 40, 42, 44, 46, & 48.