Adaptive Filter Based Two-Probe Noise Suppression System for Transient

Evoked Otoacoustic Emission Detection

MISKO SUBOTIC,1 ZORAN SARIC,1 and SLOBODAN T. JOVICIC2

1Life Activities Advancement Center, Gospodar Jovanova 35, 11000 Belgrade, Serbia; and 2School of Electrical Engineering,University of Belgrade, Bulevar Kralja Aleksandra 73, 11000 Belgrade, Serbia

(Received 24 May 2011; accepted 3 October 2011; published online 19 October 2011)

Associate Editor Berj L. Bardakjian oversaw the review of this article.

Abstract—Transient otoacoustic emission (TEOAE) is amethod widely used in clinical practice for assessment ofhearing quality. The main problem in TEOAE detection is itsmuch lower level than the level of environmental andbiological noise. While the environmental noise level can becontrolled, the biological noise can be only reduced byappropriate signal processing. This paper presents a newtwo-probe preprocessing TEOAE system for suppression ofthe biological noise by adaptive filtering. The system recordsbiological noises in both ears and applies a specific adaptivefiltering approach for suppression of biological noise in theear canal with TEOAE. The adaptive filtering approachincludes robust sign error LMS algorithm, stimuli responsesummation according to the derived non-linear response(DNLR) technique, subtraction of the estimated TEOAEsignal and residual noise suppression. The proposed TEOAEdetection system is tested by three quality measures: signal-to-noise ratio (S/N), reproducibility of TEOAE, and mea-surement time. The maximal TEOAE detection improvementis dependent on the coherence function between biologicalnoise in left and right ears. The experimental results showmaximal improvement of 7 dB in S/N, improvement inreproducibility near 40% and reduction in duration ofTEOAE measurement of over 30%.

Keywords—Otoacoustic emissions, TEOAE, Biological

noise, Adaptive noise cancelation, Hearing testing, Signal

processing.

INTRODUCTION

Analysis of otoacoustic emission (OAE) signal givesreliable information on the quality of the cochlearfunction. For this reason, methods of measurementand analysis of OAE signal have significant application

in clinical practice.19 It was demonstrated that evokedotoacoustic emissions (EOAE) are most effective,particularly transient evoked otoacoustic emission(TEOAE) and distortion product otoacoustic emission(DPOAE).

TEOAE is recognized as adequate method forhearing screening. For elicitation of otoacoustic emis-sion it uses a click stimulus of 80–88 dB peak equivalentsound pressure level (SPL).9 However, the presence ofthe stimulus artifacts and noise in ear canal, results indifficulty in identifying the true TEOAE signal. Toreduce the stimulus artifacts, Kemp12 proposed aderived non-linear response (DNLR) technique. Thistechnique was considered in this paper.

A serious problem in OAE signal measurement is itsextremely low level, between 10 and 20 dB SPL.19 Thisis significantly under the level of noise which appears inthe auditory canal. This noise may be external (ambi-ent noise) and internal (biological noise). Biologicalnoise in the auditory canal is the consequence of thefunctioning of human organism (heartbeats, breathing,functioning of internal organs, body movements, etc.)which significantly masks OAE signal, particularly atfrequencies below 1000 Hz.14 In one experiment thelevel of biological noise measured in unclosed auditorycanal was 20 dB SPL, for a healthy young individual.13

Placing the probe which hermetically closes auditorycanal increases SPL of the biological noise to 30 dBSPL.10,25

In order to provide favorable signal-to-noise ratio(S/N) and to improve reliability of OAE detection,different procedures are applied based on improvementof recording conditions, optimization of stimulus sig-nal characteristics, as well as improvement of thealgorithms for processing of OAE signal.2,15,16,18,21

Taking into account that SPL of ambient noise in quietroom is about 40 dB SPL,11 and that the acoustic

Address correspondence to Misko Subotic, Life Activities

Advancement Center, Gospodar Jovanova 35, 11000 Belgrade,

Serbia. Electronic mail: ifp2@ikomline.net, sariczoran@yahoo.com,

jovicic@etf.rs

Annals of Biomedical Engineering, Vol. 40, No. 3, March 2012 (� 2011) pp. 637–647

DOI: 10.1007/s10439-011-0430-2

0090-6964/12/0300-0637/0 � 2011 Biomedical Engineering Society

637

isolation of the probe and the earpiece is from 10 to20 dB SPL,24 the level of the acoustic noise in theauditory canal is still significantly higher than the levelof OAE. Suppression of this noise is carried out byadaptive filtering with one or more reference micro-phones.4,15 Muller and Kompis15 improved TEOAEdetection using reference microphone placed neartesting ear. Unfortunately this method is unable tocope with biological noise (authors’ note, p. 164).Delgado et al.4 used auxiliary probe placed in anotherear to record both internal and external noise referencefor adaptive filtering. Although proposed adaptive fil-tering improves DPOAE detection, it can not be sim-ply extended to TEOAE detection because of passiveringing in ear canal caused by strong pulse stimulus.This passive ringing disturbs convergence of theadaptive algorithm and distorts TEOAE signal. In thispaper we proposed adaptive filtering method capableto cope with this problem.

The subject of this paper is suppression of thebreathing noise as the strongest and constantly presentbiological noises in ear canals during TEOAE detec-tion. So far, the problem has not been systematicallyanalysed and there have been no suitable solutions.The noise reduction method proposed in this paper isbased on adaptive filtering which uses reference bio-logical noise signal recorded in the other (non-testing)ear. Proposed method takes into account passiveringing caused by strong click stimulus, and suppressesit in a way similar to DNLR technique. Specialattention was paid to prevent any distortion in esti-mation of adaptive filter coefficients by applying sub-traction of current estimate of TEOAE (‘‘Methods’’and ‘‘Subtraction of TEOAE Estimate’’ sections).Finally, additional improvement of TEOAE estimate isobtained by weighted averaging which is described in‘‘Residual Noise Suppression’’ section. Experimentalresults and benefits obtained by proposed adaptivefiltering method is presented in ‘‘Discussion’’ section.

METHODS

System Structure

Figure 1 displays block diagram of the proposedtwo-probe system for improved TEOAE detection.The system consists of preprocessing module for bio-logical noise suppression and ordinary DNLR basedTEOAE detection module. The system uses twoprobes: one, test probe for recording noisy otoacousticsignal in test ear xT(t), and another, reference probe forrecording biological noise in another (reference) earxR(t). The click stimulus, used for eliciting TEOAE(Fig. 1), is the electric pulse 80 ls wide applied to theprobe transducer which provides level of 80–88 dBpeak equivalent SPL. The probe microphone recordsall sounds in the ear canal: click stimulus, ear canalresponse to click stimulation, otoacoustic signal anddifferent kinds of noises, including biological noise.

TEOAE Detection

High intensity clicks stimuli (80–88 dB) cause pas-sive ringing which partly overlaps TEOAE signal. Toreduce passive ringing, two techniques are usuallyused: windowing and DNLR.8,24 Windowing tech-nique is based on fast decay of the click stimulus. Toseparate passive ringing and OAE the processingwindow is placed 2.5 ms after click stimulus.24 Tukeywindow with a � 0.4 is usually used (Fig. 2).

FIGURE 1. Block diagram of two-probe system with preprocessing before TEOAE detection.

FIGURE 2. Tukey window.

SUBOTIC et al.638

The DNLR technique is based on non-linear growthpattern of the TEOAE and packet of four click stim-uli.8 The packet consists of three stimuli of the samepolarity and intensity and the fourth stimulus which isthree times larger and has the opposite polarity, asnoted on Fig. 1. In the first step, the ear responses aftereach stimulus in one packet are saved in four stimulusregisters. In second step, all four stimulus registers areadded resulting in high suppression of linear compo-nents (that is click stimuli and their passive ringing)with suppression up to 40 dB.12

After registers summation only non-linear portion ofear response related to otoacoustic signals remains. Theresult for each packet is alternately added in one of thetwo memory buffers ‘‘A’’ or ‘‘B’’. Buffers ‘‘A’’ and ‘‘B’’are used for calculation of TEOAE signal,S/N and reproducibility, as valuable parameters incharacterization of TEOAE, after sufficient number ofaccumulation. Average rms (root mean squares) of thesum of buffers ‘‘A’’ and ‘‘B’’ is estimate of TEOAEsignal. On the other hand, rms of the difference betweenbuffers ‘‘A’’ and ‘‘B’’ is estimate of noise signal. Theother important criterion in the identification ofTEOAE is reproducibility.17 It represents cross corre-lation of the spectra of buffered ‘‘A’’ and ‘‘B’’ signals.

TEOAE Preprocessing

Biological noise suppression in preprocessing mod-ule (Fig. 1) is realized in two steps: by adaptive filteringand by adaptive weighting. The first step is adaptivefiltering based on proposition by Widrow andStearns,26 adapted for specific TEOAE detection con-ditions described below. This approach is presented inFig. 3. Adaptive filter exploits the coherence betweenbiological noise xR(t) recorded on the reference probe

and biological noise component of the test probe signalxT(t). Adaptive filter uses reference signal xR(t) tosuppress the biological noise in test signal.

The second processing step minimizes estimationerror by attenuation of the output signal e(t) if the S/Nis low. Inspired by the Wiener filtering, it is reasonableto apply time varying weight wk controlled by signal-to-breathing noise ratio. During inspiration and expi-ration, when noise power is high, signal-to-breathingnoise ratio is low. At this time intervals, weight wk hasto be low. Contrary, between inspiration and expira-tion wk has to tend to one. Weight wk controlled bysignal-to-breathing noise ratio provides minimummean estimation error.

Signal Model

Microphone built in test probe records test signaldenoted as xT(t), Fig. 1. Taking into account thatbiological noise in test ear is correlated with referenceprobe signal, xT(t) is

xT tð Þ ¼ hRT � xR tð Þ þ xTEOAE tð Þ þ xpr tð Þ þ nTu tð Þ;ð1Þ

where xRðtÞ is biological noise recorded in referenceprobe, hRT is impulse response of equivalent transferfunction from reference to test probe, term hRT � xR tð Þis the biological noise in test probe which is linearlydependent on reference probe signal xR tð Þ, xTEOAE tð Þ isTEOAE signal, xpr tð Þ is click stimulus and passiveringing of the test ear canal, nTu tð Þ is uncorrelatednoise component, and � is convolution operator.Terms hRT � xR tð Þ, xpr tð Þ, and nTu tð Þ, act as interfer-ence that disturb detection of TEOAE. As noted,passive ringing xpr tð Þ is efficiently suppressed by

FIGURE 3. Adaptive filtering module modified according to DNLR technique.

Adaptive Filtering in TEOAE 639

DNLR technique whereas uncorrelated noise nTu tð Þ issuppressing by averaging in buffers ‘‘A’’ and ‘‘B’’. Theremaining biological noise hRT � xR tð Þ, is suppressedby adaptive filtering method proposed in this paper.

Adaptive Filtering

Terms xpr tð Þ; xTEOAE tð Þ, and nTu tð Þ in model (1) alsodisturb estimation of the adaptive filter coefficients. Toreduce the impact of the stimulus with passive ringingxpr tð Þ on the adaptive algorithm we applied registerssummation in the similar manner as it is used inDNLR technique (Fig. 3). Additionally, to reduce theimpact of xTEOAE tð Þ to the adaptive algorithm, we usedcurrent estimate of x

kð ÞTEOAE tð Þ from TEOAE module

(see Fig. 1), and subtract it from input test probe sig-nal. Subtraction of x

kð ÞTEOAE tð Þ provides unbiased esti-

mate of the adaptive filter coefficients and improvesbiological noise suppression.

Registers Summation Method

In our adaptive filtering method, the estimation ofthe adaptive filter parameters is separated from theprocessing of the input signals xR(t) and xT(t). Coef-ficients of the adaptive filter are estimated in moduledenoted as adaptive algorithm (Fig. 3). Estimatedcoefficients are copied to the slave filter which pro-cesses xR(t) and xT(t).

In the estimation step, samples of the test signalxT(t) are gathered into stimuli registers x

k;jð ÞT which

elements are xk;jð ÞT ið Þ; i ¼ 1; . . . ;N. Index k denotes of

the stimulus packet, values j = 1, 2, 3 denote smallstimuli registers and j = 4 denotes large stimulus reg-ister in k-th packet, N denotes number of samples ineach register. Registers are added sample by sample

xkð ÞDNLR ið Þ ¼

X4

j¼1x

k;jð ÞT ið Þ; i ¼ 1; . . . ;N: ð2Þ

As xk;jð ÞT ið Þ are xT(t), we can substitute model (1) into

model (2). Applying some arithmetic transforms

xkð ÞDNLR ið Þ ¼ hRT �

X4

j¼1x

k;jð ÞR ið Þ þ

X4

j¼1x

k;jð ÞTEOAE ið Þ

þX4

j¼1x k;jð Þpr ið Þ þ

X4

j¼1n

k;jð ÞTu ið Þ; ð3Þ

where xk;jð ÞR ið Þ, x k;jð Þ

TEOAE ið Þ, x k;jð Þpr ið Þ, and n

k;jð ÞTu ið Þ denote

samples of the corresponding reference registers,TEOAE response, passive ringing, and uncorrelatednoise, respectively. Note that term

P4j¼1 x

k;jð Þpr ið Þ is

vanishing in the same manner as in DNLR method.We can rearrange (3) by

xkð ÞDNLR ið Þ ¼ hRT � x

kð ÞR ið Þ þ ~xTEOAE ið Þ þ nkTu ið Þ; ð4Þ

where

xkð ÞR ið Þ ¼

X4

j¼1x

k;jð ÞR ið Þ; ð5Þ

whereas ~xTEOAE ið Þ ¼P4

j¼1 xjð ÞTEOAE ið Þ is sum of TEOAE

responses, and n kð ÞTu ið Þ ¼

P4j¼1 n

k;jð ÞTu ið Þ is uncorrelated

noise in test probe. Note that otoacoustic emission hasthe same shape for all stimuli packets. That is why weomitted packet index k in ~xTEOAE ið Þ. Samples of thereference signal xR(t) are gathered into stimulus reg-

isters xk;jð ÞR (Fig. 3) in the same manner as signal xT(t),

and after addition of signals xk;jð ÞR ið Þ the output signal

xkð ÞR ið Þ is defined by (5).

Note that the transfer function hRT is the same forboth (1) and (4) models. Because of that the coeffi-cients of the adaptive filter hRT can be recursivelyestimated by LMS algorithm using x

kð ÞDNLR ið Þ and

xkð ÞR ið Þ as primary and reference signals.

Subtraction of the TEOAE Estimate

During adaptation process, ~xTEOAE ið Þ in (4) acts asinterference and disturbs estimation of the adaptive fil-ter. So, it is highly desirable to suppress it. We cansuppress it by subtraction its estimate from x

kð ÞDNLR ið Þ.

The estimate of ~xTEOAE ið Þ is available from TEOAEdetectionmodule which generates an estimate x

kð ÞTEOAE ið Þ

after processed packetk (Fig. 1). For the first 100 stimulipackets (k< 100) the estimate x

kð ÞTEOAE ið Þ is not accurate

enough to be used in suppression of ~xTEOAE ið Þ. After 100packets (k ‡ 100) we assume that the estimate x

kð ÞTEOAE ið Þ

is sufficiently accurate to be used for ~xTEOAE ið Þ sup-pression. We define it by

~xkTEOAE ið Þ ¼ a kð Þx k�1ð ÞTEOAE ið Þ a kð Þ ¼ 0 if k<100

1 otherwise

�;

ð6Þ

where ~xkTEOAE ið Þ is current estimate of ~xTEOAE ið Þ.

Adaptive Algorithm

The most popular adaptive filtering algorithm forthe signals with Gaussian PDF is normalized LMS(NLMS) algorithm. In our approach, this algorithmcan be described by

e ið Þ ¼ xkð ÞDNLR ið Þ � h

k;i�1ð ÞTRT x

kð ÞR ið Þ; filtering; ð7Þ

hk;ið ÞRT ¼ h

k;i�1ð ÞRT þ l

xkð ÞR ið Þ

������2

þwe ið Þx kð Þ

R ið Þ;

coefficients update;

SUBOTIC et al.640

where e(i) is output of the adaptive filter, hk;ið ÞRT ¼

hk;ið ÞRT ð0Þ; h

k;ið ÞRT ð1Þ; . . . ; h

k;ið ÞRT M� 1ð Þ

h iTis coefficient vec-

tor of adaptive filter, xkð ÞR ið Þ ¼ ½x kð Þ

R ið Þ; x kð ÞR i� 1ð Þ; . . . ;

xkð ÞR i�Mþ 1ð Þ�T, M is the filter length, l and w are

positive constants which control stability and conver-gence rate.

According to (1), where xTEOAE tð Þ and xpr tð Þ are notrandom but deterministic signals, the residual e ið Þ isnot Gaussian. Because of that it is reasonable to applyan alternative robust estimator instead of ordinaryNLMS algorithm. Among different robust algorithmsthe best result is obtained by sign error algorithm,6 inwhich error e(i) is replaced by its sign. Adaptation stepfor this algorithm is

hk;ið ÞRT ¼ h

k;i�1ð ÞRT þ l sign½e ið Þ� x kð Þ

R ið Þ: ð8Þ

Finally, estimated coefficient vector hk;Nð ÞRT of adap-

tive filter has to be copied into the slave filter to processreference signal xR tð Þ, as it is depicted in Fig. 3.

Residual Noise Suppression

In order to reduce residual noise we apply weight wk

that minimizes mean square estimation error in amanner similar to that implemented as by a Wienerfilter. Weight wk has to be lower if the signal-to-bio-logical noise ratio is lower. Weights wk are estimatedtaking into account high correlation of the powers ofthe breathing noises recorded in left and right earcanal, by

wk ¼ min~k

r2R kð Þ ; 1

!; r2

R kð Þ ¼ 1

N

XN

i¼1xðkÞR ið Þ

� �2;

ð9Þ

where ~k is experimentally determined positive con-stant, and r2

R kð Þ is power of the biological noise esti-mated in reference probe. The output of the adaptivefilter is weighted by wk (Fig. 1), and

sTEOAE ið Þ ¼ wkeT ið Þ: ð10Þ

Summary of the Preprocessing Algorithm

Preprocessing algorithm for biological noise reduc-tion is summarized in Fig. 4.

Test Methods

Twenty-one volunteers participated in this study.Written consent was obtained from all participantsprior to testing. The test protocol was reviewedand approved by ethical committee of the Institute

for experimental phonetics and speech pathology(Belgrade, Serbia) where all measurements were con-ducted. Experiments were performed with normallyhearing subjects without pulmonary problems.

Tests conditions were defined so to imitate realrecording and processing situation with two probes. Inaddition, we wanted to conduct tests under full controlof the ratio betweenTEOAEandbiological noise power.

In the first phase of the experiment, TEOAEresponses of each subject were recorded in a quietroom in which SPL of the ambient noise was SPL = 30dBA. TEOAE measurements of the left (test) ear wererecorded and stored as audio files with sampling fre-quency fs = 48 kHz. During the process of recordingthe subjects were asked to be as quiet as possible.

In the second phase of the experiment, subjects’biological noises from left and right ear were recordedsimultaneously with two probes and stored as two-channel audio files. During the recording process thesubjects were asked to breathe deeply.

For each subject, the biological noise recorded onthe left probe was digitally mixed with previouslyrecorded unprocessed TEOAE responses formingnoisy test signal xT(t). Signal recorded on the reference(right) probe was used as noise reference xR(t).

Nine subjects with high TEOAE were selected to bethe ‘‘gold standard’’ for experimental testing.22 Meanvalues of SNR and reproducibility calculated on thetest group were SNRmean = 15.8 dB and (Reproduc-ibility)mean = 91.05%, while the corresponding stan-dard deviations were SNRstdev = 5.5 dB and(Reproducibility)stdev = 7.13%.

Subjects with strong biological noise were excludedfrom the experiment. Biological noise recorded simul-taneously with TEOAE measurement cannot be sup-pressed by the proposed adaptive filtering because ithas been recorded as single-channel signal. Namely,our two-probe noise suppression algorithm needs ref-erence noise recorded in another ear canal. Under thistest condition, our system can only suppresses addedbreathing noise, but it can not suppress biologicalnoise recorded with single-channel TEOAE signal.Because of that, subjects with strong biological noiseprevent objective evaluation the performance of theproposed noise suppression algorithm.

Processing was off-line in MATLAB environment.In our experiments we used data frames 590 sampleslong (about 12.3 ms). Data frames were positioned 110samples (about 2.5 ms) after onset of the stimuli. Theadaptive filter had M = 41 taps. Signal x

kð ÞDNLR ið Þ is

delayed by 20 samples (half of the order of the adaptivefilter) before processing by adaptive filter. It provides agood modeling of the transfer function hRT for bothpositive and negative phase shift between left and rightauditory input signals.

Adaptive Filtering in TEOAE 641

RESULTS

Breathing Noise Analysis

The breathing sounds depend upon several factors,such as airflow, inspiration and expiration phases, siteof recording, and degree of voluntary control.3 Thesource of these sounds is the same but transmissionpaths to the ear canals are different and it is reasonableto expect some differences in its characteristics.Figure 5a displays a typical subject breathing wave-forms. The power of breathing noise varies in time,according to the inspiration and expiration, at theaverage acoustical level of about 55 dBA.

The long-term power spectral densities (PSDs) ofbiological noise in both ears are shown in Fig. 5b.PSDs decay in higher frequencies and for frequenciesover 4000 Hz they are under noise floor.

Similarity of the biological noises recorded in leftand right probes can be assessed by coherence func-tion. The magnitude squared coherence (MSC) func-tion c2LR fð Þ is20

c2LR fð Þ ¼ /LR fð Þj j2

/LL fð Þ/RR fð Þ ; ð11Þ

where /LL fð Þ and /RR fð Þ are power spectral densities(PSDs) of the biological noise in left and right earcanal, /LR fð Þ is corresponding cross power spectraldensity (CPSD), f is frequency. Figure 6 displaysmagnitude squared coherence function c2LR fð Þ in termof frequency f for the signals displayed in Fig. 5a.

Coherence function (11) provides useful informa-tion about maximal potential noise suppression byadaptive filter. Maximal potential noise suppressionNSmax(f) is,

23

FIGURE 4. Preprocessing algorithm for biological noise reduction.

SUBOTIC et al.642

NSmax fð Þ ¼ 10 log10/TT fð Þ/nn fð Þ

� �¼ �10 log10 1� c2LR fð Þ

� ;

ð12Þ

where /TT fð Þ is PSD of primary input signal xT(t), and/nn fð Þ is PSD of the residual noise after processing byadaptive filter. Figure 7a shows NSmax(f) calculated fordata displayed in Fig. 6. As TEOAE analysis is con-ducted in six frequency bands (defined in ‘‘Discussion’’section), NSmax(f) is averaged in these bands andshown in Fig. 7b. The additional band with centralfrequency 724 Hz is also included in analysis. Fig. 7bindicates that the improvement obtained by adaptivefiltering is limited to about 6 dB at low frequencies.

TEOAE Analysis

To show the impact of the particular processing stepson final TEOAE detection, we tested TEOAE detectionwith the steps denoted as follows: (WP)—without pre-processing, (AF)—with adaptive filtering by sign erroralgorithm (8) using Registers summation method,(AF + TEOAEsub)—Subtraction of the TEOAE esti-mate method, (AF + TEOAEsub + WA)—adaptivefiltering by method (AF + TEOAEsub) followed byweighted averaging described in ‘‘Residual Noise Sup-pression’’ section.

For comparison the results in this paper, Fig. 8adisplays averaged memory buffers A and B of the

typical subject recorded in quiet room and processed incondition WP. The corresponding TEOAE and noisePSD estimates are displayed in Fig. 8b.

Figures 9a and 9b display averaged memory buffersA and B in presence of the biological noise obtainedwithout preprocessing (WP) and with preprocessing(AF + TEOAEsub + WA), respectively. The pres-ence of biological noise distorts the correlationbetween the memory buffers A and B (compareFigs. 8a and 9a) and may cause miss detection of theTEOAE. The corresponding estimates of TEOAE and

FIGURE 5. Breathing noise recorded in left and right ear:(a) time waveforms (inspiration and expiration intervals aredenoted by insp. and exp., respectively), and (b) the long-termPSDs.

FIGURE 6. The coherence function of the biological noisesfrom Fig. 4.

FIGURE 7. Maximal potential noise suppression: (a) contin-uous NSmax(f) and (b) NSmax(f) in frequency bands.

Adaptive Filtering in TEOAE 643

noise PSDs are displayed in Figs. 10a and 10b,respectively. Note that the best improvement inTEOAE spectrum estimate (e.g., noise suppression) isin the lower frequency range. The improvement inhigher frequency range (over 3500 Hz) is negligible.The reason for this is that power spectral density ofbiological noise is too low at higher frequencies (seeFig. 5b) compared to other noise sources present in therecording system.

DISCUSSION

Experiments are carried out to compare the contri-bution of each noise suppression method in overallTEOAE improvement. Improvement was quantifiedby (a) reproducibility and SNR of TEOAE responseimprovement, and (b) decrease of the TEOAE acqui-sition time.

The quality of TEOAE detection algorithm can beassessed by SNR and reproducibility averaged in thewhole defined spectral range or by SNR and repro-ducibility averaged in some previously defined fre-quency sub-bands.5 In our TEOAE analysis, SNR andreproducibility are estimated and averaged in six halfoctave bands with central frequencies 724, 1024, 1448,2048, 2896, and 4096 Hz (frequencies are defined bysignal processing requirements and are close toaudiometric standard frequencies). Full band averagedSNR for frequencies between 610 and 4870 Hz is alsoincluded. Table 1 displays contribution of each pre-processing step in SNR improvement. SNR is averaged

for nine test subjects. Table 2 displays correspondingaveraged reproducibility obtained by each prepro-cessing method.

Tables 1 and 2 show that the preprocessing methodssuggested in this paper, improve SNR and reproduc-ibility in lower frequency bands (with central fre-quencies from 724 to 2048 Hz). Improvement in higherfrequency bands (with central frequencies 2896 and4096 Hz) is minimal. The results are consistent withtheoretical expectations displayed in Fig. 7b.

FIGURE 8. An example of TEOAE estimate when signals arerecorded in quiet room with negligible biological noise (a) thewaveform of the buffers A and B, and (b) spectrum of TEOAEand noise.

FIGURE 9. The waveforms of averaged buffers A and B inpresence of biological noise: (a) without preprocessing, and(b) with preprocessing.

FIGURE 10. An example of TEOAE (red) and noise (blue)PSD estimates in presence of biological noise: (a) withoutpreprocessing, and (b) with preprocessing.

SUBOTIC et al.644

To compare proposed methods, the global SNR andreproducibility were averaged in the band from 724 to2048 Hz in which TEOAE improvement is most sig-nificant (Table 3). Single factor ANOVA was per-formed to statistically test results with and withoutnoise suppression methods. Significance level was setto 0.01. It turns out (Table 4) that in all cases, repro-ducibility improvement is highly statistically significant(p< 0.005) and that improvement of SNR is statisti-cally significant for the case (AF + TEOAEsub +WA). Improvement of the SNRmean and the (Repro-ducibility)mean is shown in Table 5. The biggestimprovement is at the lowest frequencies and decreasesat higher frequencies.

Another important feature is duration of theTEOAE measurement. It is desirable that the mea-surement time is as short as possible. Measurementtime is particularly critical for hearing screening forwhich is necessary to get reliable information onwhether a person passed or failed the test in theshortest period of time. However, there is the questionof criteria on which decision will be made. The criteriaare a combination of values of SNR and reproduc-ibility of the signal.5

Since there are no exactly defined criteria, we con-sidered that the subject has passed TEOAE test ifSNR> 3 dB in at least three of the five sub-bands andif (Reproducibility)> 65% in the same at least three offive sub-bands.27 In our experiments the averaged timefor TEOAE detection without preprocessing (WP) was72 s, whereas averaged detection time for method(AF + TEOAEsub + WA) was 65 s. Hence, mea-surement time is reduced 9.72%. Using the same cri-teria in at least 4 of 6 sub-bands, measurement timereduction is 32%.

Finally, we wanted to check if the proposed pre-processing methods may cause some false TEOAEdetection. For this purpose we used 2 and 1 cc cavityto simulate human ear without TEOAE response inwhich we put test probe.1,7 Then we generated ordin-ary TEOAE eliciting stimuli and recorded responsesignal. The procedure of signal recording was the sameas it was described in ‘‘Test Methods’’ section. Then weprocessed xT(t) and xR(t) by proposed preprocessingmethods followed by TEOAE detection module(Fig. 1). As expected, there was no false TEOAEdetection.

CONCLUSION

Biological noise considerably disturbs reliability ofTEOAE detection. In this paper we presented a

TABLE 1. SNR (dB) averaged on nine subjects.

Preprocessing

conditions

Central frequency (Hz)

724 1024 1448 2048 2896 4096

Full

band

WP

Mean 22.84 4.13 2.00 4.52 6.64 2.12 4.39

SD 3.10 7.24 2.62 3.49 5.74 4.87 3.88

AF

Mean 4.05 4.51 6.50 5.18 6.40 2.35 5.85

SD 3.66 8.04 1.65 1.94 5.40 4.54 3.23

AF + TEOAEsub

Mean 3.94 5.61 7.33 5.43 6.70 2.57 6.23

SD 3.90 7.68 2.24 1.80 5.65 4.45 3.35

AF + TEOAEsub+ WA

Mean 4.28 7.76 6.49 6.48 5.27 2.12 6.17

SD 4.46 7.37 3.01 2.23 6.09 4.49 3.64

WP, without preprocessing; AF, with adaptive filtering; AF +

TEOAEsub, with adaptive filtering and subtraction of the TEOAE

estimate; AF + TEOAEsub + WA, with adaptive filtering and sub-

traction of the TEOAE estimate followed by weighted averaging;

Mean, mean value; SD, standard deviation.

TABLE 2. Reproducibility (%) averaged on nine subjects.

Preprocessing

conditions

Central frequency (Hz)

724 1024 1448 2048 2896 4096

Full

band

WP

Mean 36.04 56.98 58.71 70.60 76.34 50.50 58.19

SD 20.04 34.31 24.41 19.41 19.67 30.31 17.26

AF

Mean 81.57 71.84 82.69 74.01 76.04 53.39 73.26

SD 17.27 28.33 4.78 9.06 16.86 28.83 11.35

AF + TEOAEsub

Mean 83.60 77.11 84.87 75.56 76.69 55.77 75.60

SD 14.29 23.33 5.19 8.11 17.29 28.08 10.04

AF + TEOAEsub + WA

Mean 75.57 84.40 78.32 80.32 69.58 51.83 73.34

SD 15.95 11.87 15.88 9.36 21.94 29.42 11.98

TABLE 3. Mean values of SNR and reproducibility for thespecified frequency band.

Preprocessing

conditions

SNR (dB)

band 724–2048 Hz

Reproducibility (%)

band 724–2048 Hz

WP

Mean 2.94 55.58

SD 2.81 18.08

AF

Mean 5.41 77.53

SD 2.39 8.47

AF + TEOAEsub

Mean 5.91 80.29

SD 2.55 7.47

AF + TEOAEsub + WA

Mean 6.64 79.65

SD 2.61 8.57

Adaptive Filtering in TEOAE 645

method for biological noise suppression aimed toimprove reliability of TEOAE detection. We analyzedthe influence of the breathing noise as the most dom-inant biological noise but the results may be extendedto the other biological noise such are swallow (gulp),heart sound and so on. Preliminary analysis showedstrong correlation between breathing noise recorded inleft and right ear canals. This property of the breathingnoise indicates that it can be suppressed by adaptivefiltering. The proposed noise suppression system actsas preprocessing module followed by standard DNLRbased TEOAE detection module.

Two problems disturb estimation of the adaptivefilter coefficients: passive ringing in ear canal after clickstimulus, and TEOAE signal itself. Firstly, the influ-ence of the passive ringing is reduced by four registerssummation similarly as it is done in DNLR technique.This method provides unbiased estimate of adaptivefilter coefficients. Secondly, TEOAE signal, which alsodisturbs estimation of the adaptive filter coefficients, issuppressed using its estimate. Additionally, residualnoise after adaptive filtering is weighted according tothe estimated noise power. The proposed TEOAEdetection system was tested by three quality measures:S/N, reproducibility of TEOAE, and measurementtime. According to the spectral distribution of thebreathing noise and frequency dependence of thecoherence function, TEOAE signal improvement issignificant for frequency bands under 2500 Hz.Experimental results show up to 7 dB improvement inS/N, improvement in reproducibility near 40% andreduction in duration of TEOAE measurement over

30% (depending on passing criterion for TEOAE test).These results are important for clinical application andincorporation of the proposed preprocessing system instandard TEOAE equipment can improve its usage.

Computational consumption of the proposed pre-processing algorithm is approximately same as the con-sumption of the adaptive filter. The order of the adaptivefilter is low, only forty-one taps. Hence, the proposedpreprocessing algorithm can be implemented in anycontemporary low cost DSP chipset.

ACKNOWLEDGMENTS

This research was supported by Grants 178027 andTR32032 from the Ministry of Science and Techno-logical Development of the Republic of Serbia.

CONFLICT OF INTEREST

The authors of the above paper state that they haveno conflict of interest.

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