Novel Speech Signal Enhancement Techniques For Tamil Speech . - IJETTCS
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.Novel Speech Signal Enhancement Techniquesfor Tamil Speech Recognition using RLSAdaptive Filtering and Dual Tree ComplexWavelet TransformVimala.C 1, Radha.V22Professor and Head, Department of Computer Science,Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatore – 641043, Tamil Nadu, India.AbstractA good speech signal enhancement technique must improveboth quality and intelligibility of the enhanced signals for alltypes of environment conditions. However, the speech signalenhancement technique can reduce noise, but introduce itsown distortion to the enhanced signals. Hence, it may or maynot improve the quality and the intelligibility of the enhancedspeech signals. The main objective of this paper is to proposesuitable speech signal enhancement techniques that canimprove both quality and intelligibility of the enhancedsignals. In this research work, the combinational speechsignal enhancement techniques are proposed using Dual TreeComplex Wavelet (DTCW) Transform and Recursive LeastSquares (RLS) adaptive filtering. Three types of techniquesare introduced by using the combination of DTCW and RLSadaptive filtering. The performances of the developedtechniques are evaluated based on both subjective andobjective speech quality measures. The experimental resultsprove that the proposed methods have provided better resultsin speech noise cancellation. Excellent results are achieved inimproving the quality and intelligibility of the enhancedspeech signal.Keywords: Speech signal enhancement, Dual TreeComplex Wavelet (DTCW) Transform, RLS adaptivealgorithm, Ideal Binary Mask (IBM), Phase SpectrumCompensation (PSC).1. INTRODUCTIONIn real time environment, the speech signals are corruptedby several forms of noise such as competing speakers,background noise, channel distortion and roomreverberation etc. The presence of background noise inspeech significantly reduces its quality and intelligibilityof the signal. Therefore, enhancing the noisy speechsignal is necessary for improving the perceptual quality.Speech signal enhancement is applied in manyapplications like telecommunications, speech and speakerrecognition etc. [1]. Particularly, there is a huge need forspeech signal enhancement in speech recognition system.This is because, speech recognition application may bedeveloped in one environment and it can be operated inVolume 6, Issue 1, January – February 2017some other environment. In such cases, the mismatchbetween the training and testing conditions will beincreased and the recognition performance will bedecreased. Several techniques have been proposed forspeech signal enhancement such as spectral subtraction,adaptive filtering, Kalman filtering, wavelet filtering andIdeal Binary Mask (IBM) etc.The main objective of this paper is to implement efficientspeech signal enhancement techniques which are suitablefor different noisy conditions. The potent metrics of theDTCW transform has been considered and it is combinedwith RLS adaptive filtering, IBM and Power SpectrumCompensation (PSC) methods. Four types of noise (White,Babble, Mall and Car) and five types of SNR dB levels(-10dB, -5dB, 0dB, 5dB and 10dB) are involved in theproposed work and their performances are evaluated bothsubjectively and objectively. In this paper, apart fromnoise reduction, the improvement in the quality andintelligibility of the enhanced signal has been focusedmore. The proposed techniques have improved bothintelligibility and the quality of the enhanced signal.The paper is organized as follows. Section 2 discussesabout the related works on wavelet transform. Section 3explains the RLS adaptive filtering technique and section4 discusses about the proposed technique using DTCWtransform. In section 5, the experimental results arepresented and the performance metrics used for theproposed work is explained in section 6. The overalldiscussions are summarized in section 7 and theconclusion and future work is given in section 8.2. RELATED WORKSReshad Hosseini and Mansur Vafadust, (2008) havedeveloped an almost perfect re-construction filter bank fornon-redundant, approximately shift-invariant, complexwavelet transforms [2]. The proposed novel filter bankwith Hilbert pairs wavelet filters does not have seriousdistributed bumps on the wrong side of power spectrum.The redundancy of an original signal is significantlyPage 150
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017.reduced and the properties of proposed filter bank can beexploited in different signal processing applications.Slavy, G. Mihov et al. (2009) performed a de-noising ofnoisy speech signals by using Wavelet Transform [3]. Theuse of wavelet transform in de-noising and the speechsignals contaminated with common noises is investigated.The authors state that, the wavelet-based de-noising witheither hard or soft thresholding was found to be the mosteffective technique for many practical problems. Theexperimental results with large database of referencespeech signals contaminated with various noises in severalSignal-to-Noise Ratio (SNR) dB conditions are presented.The authors also insist that, the power spectrumestimation using a wavelet based de-noising may beapplied as an important approach for better speech signalenhancement. The research work will be extended to beapplied for the practical research on speech signalenhancement for hearing-aid devices.Rajeev Aggarwal et al. (2011) have implemented aDiscrete Wavelet Transform (DWT) based algorithmusing both hard and soft thresholding for denoising [4].Experimental analyzes is performed for noisy speechsignals corrupted by babble noise at 0dB, 5dB, 10dB and15dB SNR levels. Output SNR and MSE are calculatedand compared using both types of thresholding methods.Experiments show that soft thresholding method wasfound to be better than a hard thresholding method for allthe input SNR dB levels involved in the work. The hardthresholding method has extended a 21.79 dBimprovement while soft thresholding has achieved amaximum of 35.16 dB improvement in output SNR.Jai Shankar, B and Duraiswamy, K (2012), have proposeda de-noising technique based on wavelet transformation[5]. The noise cancellation method is improved by aprocess of grouping closer blocks. All the significantinformation resides in each set of blocks are utilized andthe vital features are extracted for further process. All theblocks are filtered and restored in their original positions,where the overlapping is applied for grouped blocks. Theexperimental results have proved that the developedtechnique was found to be better in terms of both SNR andsignal quality. Moreover, the technique can be easilymodified and used for various other audio signalprocessing applications.D. Yugandhar, S.K. Nayak (2016) have proposed a natureinspired population based speech enhancement techniqueto find the dynamic threshold value using TeachingLearning Based Optimization (TLBO) algorithm by usingshift invariant property of DTCWT [6]. The performanceof the proposed methods is better in terms of PESQ andPSNR.Pengfei Sun and Jun Qin (2017) have proposed a two-Volume 6, Issue 1, January – February 2017ISSN 2278-6856stage Dual Tree Complex Wavelet Packet Transform(DTCWPT) based speech enhancement algorithm, inwhich a Speech Presence Probability (SPP) estimator anda generalized Minimum Mean Squared Error (MMSE)estimator are developed [7]. In their work, to overcomethe drawback of signal distortions caused by downsampling of Wavelet Packet Transform (WPT), a twostage analytic decomposition concatenating UndecimatedWavelet Packet Transform (UWPT) and decimated WPTis employed. The process of RLS adaptive filteringtechnique is explained in the next section.3. RLS ADAPTIVE FILTERINGSIGNAL ENHANCEMENTFORSPEECHRLS adaptive algorithm is a recursive implementation ofthe Wiener filter, in which the input and output signalsare related by the regression model. RLS has the potentialto automatically adjust the coefficients of a filter, eventhough the statistic measures of the input signals are notpresent [8]. In RLS algorithm, filter tap weight vector isupdated byw(n) w T (n 1) k ( n)en 1 ( n)(1)The steps involved in RLS adaptive algorithm is given inthe following algorithm and the variables used in thealgorithm is illustrated in Table 1.Algorithm of RLS adaptive filteringStep 1: Initialize the algorithm by settingwˆ (0) 0,P(0) 1 I , and Small positive Large positiveconstantconstantfor high SNRfor low SNRStep 2: For each instant time, n 1,2, , compute 1P(n 1)u (n)k ( n) 1 1u H (n) P(n 1)u (n)y (n) wˆ H (n 1)u (n)e(n) d (n) y (n)wˆ (n) wˆ (n 1) k (n)e* (n)P(n) 1P(n 1) 1k (n)u H (n) P(n 1)When the input data characteristics are changed, the filteradapts to the new environment by generating a new set ofcoefficients for the new data [9]. Here, λ-1 denotes thereciprocal of the exponential weighting factor. The mainadvantage of RLS adaptive filtering is, it attempts toreduce the estimated error e(n). Therefore, output from theadaptive filter matches closely the desired signal d(n). Theperfect adaptation can be achieved, when e(n) reachesPage 151
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017.zero. In this work, the resultant enhanced signal y(n)produced by RLS filtering was found to be better in termsof quality and intelligibility.Table 1: Variables used in RLS AlgorithmVariableNDescriptionCurrent algorithm iterationu(n)Buffered input samples at step nP(n)Inverse correlation matrix at step nk(n)Gain vector at step ny(n)Filtered output at step ne(n)Estimation error at step nd(n)Desired response at step nΛExponential memory weighting factorVimala.C and Radha.V have done a performanceevaluation of the three adaptive filtering techniques,namely, Least Mean Squares (LMS), Normalized LeastMean Squares (NLMS) and RLS adaptive filteringtechniques. These techniques are evaluated for NoisyTamil Speech Recognition based on three performancemetrics, namely, SNR, SNR Loss and MSE [10]. It isobserved from the experiments that, RLS techniqueprovides faster convergence and smaller error, but itincreases the complexity when compared with LMS sandNLMS techniques. Based on the significant resultachieved by the RLS adaptive filtering, the combinationaltechniques are proposed by using the DTCW transformbased reconstruction methodology. The subsequentsections briefly explain the same in detail.4. PROPOSED TECHNIQUE USINGFILTERING AND DTCW TRANSFORMRLSIn signal processing, quality represents the naturalness ofspeech,and the intelligibility represents theunderstandability of text information present in the signal.However, removing noise and improving the perceptualquality and intelligibility of a speech signal, withoutaltering the signal quality, is a crucial job. This is animportant problem in any speech enhancement technique.The main objective of this research work is, to developefficient speech enhancement techniques which canimprove both quality and intelligibility of the enhancedspeech signals. To meet this objective, two significantimprovements are done with the existing RLS adaptivealgorithm. Suitable square root correlation matrix andforgetting factor value is identified which can beapplied for all type of noises and SNR dB levels,and The reconstruction methodology is applied to theresultant RLS signal using DTCW transform, toVolume 6, Issue 1, January – February 2017ISSN 2278-6856produce the perfect enhanced signal as like theoriginal input signal.Various initial square root correlation matrix values andRLS forgetting factor values have been evaluated. It isobserved from the experiments that different forgettingfactor value and square root correlation matrix need to beassigned for positive and negative SNR dB values. In suchcases, these values to be assigned and their performancesshould be evaluated based on trial and error method. It istime consuming and not suitable for applying differentnoisy conditions. Therefore, the above mentioned twoparameters are fine-tuned, to provide optimal valueswhich are more suitable for both positive and negativeSNR dB levels. The experiments are carried out underMatlab environment and the desired values for the aboveparameters are discovered. It is confirmed from theexperimental outcome that, better results are obtainedwhen the initial square root correlation matrix values areassigned as 2*eye(10) and RLS forgetting factor value isset to 1. After fine tuning these two parameters, thereconstruction methodology is implemented using DTCWtransform. The advantages of using DTCW transform andthe steps involved in the proposed technique are explainedin the next section.4.1 Advantages of using DTCWThe standard form of Discrete Wavelet Transform (DWT)is shift-variant, which is undesirable and does not provideperfect speech signal enhancement. To perform speechenhancement for severe noisy conditions, the simple DWTcannot produce the expected outcome. In such cases, thereis a need for other alternative technique which canperform well under different noisy conditions. Toovercome the shift-variance problem, noise reductionsbased on shift-invariant wavelet transforms, have beenintroduced by using DTCW transform [9]. It consists oftwo specifically designed DWTs, which are applied inparallel to the same input data and it is shown in Figure 1[10]. DTCW transform is more attractive than the singleDWT in terms of computational complexity, because it isequivalent to the two standard DWTs [13].Figure 1 Structure of DTCW TransformPage 152
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017.The sub-band signals of these two DWTs can beinterpreted as the real and imaginary parts of a complexwavelet transform, which is nearly shift-invariant. InDTCW transform, the real and imaginary coefficients arecalculated in tree a and tree b respectively [14]. In thisresearch work, the enhanced signal produced by RLSadaptive filtering is reconstructed using DTCW transform.Figure 2 shows the proposed speech enhancementtechnique using RLS-DTCW Transform and its steps arebriefly given in the following algorithm.ISSN 2278-6856Steps involved in ProposedRLS-DTCW TransformStep 1: Get the noisy speech signal as an input,Step 2: Initialize RLS filtering,Step 3: Fine tune the Initial square root correlationmatrix inverse,Step 4: Set RLS forgetting factor value to 1,Step 5: Perform RLS filtering,Step 6: Pass the resultant signal as an input to theDTCW transform,Step 7: Initialize the DTCW transform forperforming reconstruction methodology,Step 8: Calculate the complex transform of a signalusing two separate DWT decompositions(tree a and tree b),Step 9: Extract the real coefficients using tree a,Step 10: Extract the imaginary coefficients usingtree b,Step 11: Approximate shift-invariance, andStep 12: Do perfect reconstruction of the signal.5. EXPERIMENTAL RESULTSFigure 2 Proposed speech enhancement technique usingRLS-DTCW TransformAs given in the algorithm, initially the noisy input signalis passed to the adaptive filtering. Later the parameters arefine tuned and the suitable values are assigned for speechenhancement. Subsequently, the output signal acquiredfrom RLS filtering is further given for DTCW transformto perform reconstruction methodology. By using DTCWtransform, additional information about the noisy inputsignal can be extracted, because it involves both real struction and better signal enhancement is achieved.The resultant signal has produced better signalenhancement and it is very much close to the originalsignal. The experimental results indicate that, the RLSadaptive filtering with DTCW transform has performedbetter when compared with the existing RLS adaptivefiltering. The experimental result achieved by thedeveloped technique is presented in the next section.Volume 6, Issue 1, January – February 2017The perception of a speech signal is usually measured interms of its quality and intelligibility. Quality is thesubjective measure which reflects on individualpreferences of listeners. Intelligibility is an objectivemeasure which predicts the percentage of words that canbe correctly identified by the listeners. It is noticed fromthe experimental results that, the resultant signals ofRLS was found to be better in terms of both quality andintelligibility. Since, the perfect reconstruction is done,the enhanced signals are found to be more clear andnatural.The experiments are done with 10 Tamil Spoken Digitsuttered 10 times which are corrupted by four types of noise(White, Babble, Mall and Car noise) and five types ofSignal-to-Noise Ratio (SNR) dB levels varying from-10dB to 10dB. The total dataset size is 2000(10*10*4*5). Since the noisy dataset is not available forTamil language, it is created artificially by adding noisefrom NOIZEUS database. In noisy environment, when theSNR dB level is less than 20 dB, the speech recognitionwill become a difficult problem. In this research work,even more critical situations are handled. Figure 3 showsthe waveform representation of the enhanced signalscorrupted by babble noise using proposed RLS-DTCWtransform and the corresponding spectrograms arepresented in Figure 4.Page 153
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.Figure 3 Waveform Representation of the Enhanced Signalusing RLS-DTCW Transform TechniqueAs discussed in the previous section, next to RLS, theIBM and PSC methods have produced better results.Based on the improvements achieved with thecombination of RLS and DTCW transform, the IBM andPSC methods are also considered for improving theirperformance. Therefore, these two methods are alsoimproved by combining withFigure 4 Spectrogram Representation of the Enhanced Signalusing RLS-DTCW Transform Techniquethe RLS-DTCW transform. To accomplish this task, thefiltered signal using IBM and PSC methods are passed tothe RLS and DTCW transform. The performanceimprovement of the IBM and PSC methods are assessed intwo ways: By applying the DTCW reconstructionVolume 6, Issue 1, January – February 2017Page 154
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856. methodology alone (IBM-DTCW),(PSC-DTCW), and By applying both DTCW and RLS adaptiveFiltering technique (IBM-RLS-DTCW),(PSC-RLS-DTCW).The overall approach of the proposed work is given inFigure 5. It is observed from the experimental outcomesthat, there was a reasonable improvement achieved byusing DTCW transform for both IBM and PSC methods.However, there was a significant performanceimprovement obtained while using both RLS and DTCWtransform rather than using DTCW transform alone.Particularly, the PESQ, MOS values has been increasedand the WSS and MSE values have been reducedextensively. Performance evaluation of RLS-DTCW, IBMRLS-DTCW and PSC-RLS-DTCW techniques based onspeech signal quality measures are discussed below.6. Performance EvaluationsSpeech Signal Quality MeasuresbasedonThe developed speech signal enhancement techniques areevaluated by using both subjective and objective speechquality measures. In this work, six types of objectivequality measures and one subjective quality measure isconsidered.6.1 Objective Speech Quality MeasuresObjective metrics are evaluated, based on themathematical measures. The objective quality measuresused in this work are as follows:Weighted Spectral Slope (WSS)Segmental SNR (SegSNR),Output SNR, andMean Squared Error (MSE).6.1.1 Perceptual Evaluation of Speech Quality (PESQ)PESQ is the most sophisticated and accurate speech signalquality measure. It is recommended by ITU-T for speechquality assessment of 3.2 kHz narrow-band handset fortelephony and speech codec applications. To computePESQ, the difference between the original and theenhanced signals are computed and averaged over time.The prediction of subjective quality rating between 1.0 and4.5 will be produced. The higher value represents thebetter quality of the enhanced signal.6.1.2 Log Likelihood Ratio (LLR)LLR is computed with respect to the difference betweenthe target and the reference signals in frame-by-frameanalysis. LLR computation requires the correspondingoriginal speech signal as the reference signal forcomparing with the target signal and it is given by a p R c a pTLLR ( a p , a c ) log a R aT c c cwhere,ap (2)ac is the LPC vector of the original speech frame,Ris the LPC vector of the enhanced speech frame, c isthe autocorrelation matrix of the original speech signal. Perceptual Evaluation of Speech Quality (PESQ), Log Likelihood Ratio (LLR),Figure 5 Overall Approach of the Proposed Workbetween the adjacent spectral magnitudes in decibels.6.1.3 Weighted Spectral Slope (WSS)WSS is measured based on the comparison of the WSS computation is given bysmoothed spectra from the clean and distorted speechsamples. The spectral slope is obtained as the differenceVolume 6, Issue 1, January – February 2017Page 155
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.(3)where, WWSS(j,m) are the weights, K 25, M is the numberof data segments, and Sc(j,m) and Sp(j,m) are the spectralslopes for the jth frequency band of the clean and enhancedspeech signals, respectively.6.1.4 Segmental SNR (SegSNR)SegSNR represents the average measurements of SNRover short good frames. The SegSNR computation is givenby(4)where, x(n) is the input signal, xˆ(n) is the processedenhanced signal, N is the frame length and M is thenumber of frames in the signal.6.1.5 Output SNRSNR is defined as the power ratio between the clean signaland the background noise. The SNR can be computed forboth input and output signals. SNR is defined bySNR 10 log 10PsPnM 1 N 1 Table 2: Signal and Background Distortion Scale RatingRating(5)where, Ps and Pn represents the average power of speechsignal and noisy signal respectively. An output SNRrepresents the relationship between the strength of theoriginal and the degraded speech signal expressed indecibels and it is computed after applying the speechsignal enhancement techniques. Ideally, the greater SNRindicates that the speech is stronger than the noise. Anefficient technique should improve the value of the outputSNR for the enhanced signal.6.1.6 Mean Squared Error (MSE)The MSE measure is defined by1MSE MN6.2 Subjective Speech Quality MeasureSubjective quality evaluations are performed by involvinga group of listeners to measure the quality of the enhancedspeech. The process of performing MOS is describedbelow.Mean Opinion Score (MOS)MOS predicts the overall quality of an enhanced signal,based on human listening test. In this work, instead ofusing a regular MOS, the composite objective measuresintroduced by Yang Lu and Philipos, C. Loizou (2008) isimplemented [15]. The authors have derived new accuratemeasures from the basic objective measures, which areobtained by using multiple linear regression analysis andnonlinear techniques. It is time consuming and costeffective but provides more accurate estimate of the speechquality, so it is considered in this research work. Separatequality ratings for both signal and background distortionsare used and it is shown in Table.2. To calculate the MOS,the listeners have to rate the particular enhanced speechsignal, based on the overall quality. The overall quality ismeasured by calculating the mean value of signal andbackground distortions (1 bad, 2 poor, 3 fair, 4 goodand 5 excellent). In this research work, the MOS iscalculated by performing listening test from 20 differentspeakers (10 males and 10 females). The listeners wereasked to rate the speech sample under one of the fivesignal quality categories.54321Signal DistortionScalePurely Natural, NodegradationFairly Natural,Slight degradationSomewhat natural,Somewhat degradedFairly unnatural,Fairly degradedQuite unnatural,Highly degradedBackgroundDistortion ScaleNot perceptibleSomewhatnoticeableNoticeable but notintrusiveFairly Noticeable,SomewhatIntrusiveQuite Noticeable,Highly intrusive2X ( l ) Xˆ k2 ( l )2k l 0 k 0(6)where,is the short time MSS of the clean signal,isthe estimated MSS, N is the total number of frequencybins. The small values of MSE show the better estimate ofthe true MSS.Volume 6, Issue 1, January – February 2017The experimental results obtained by the adoptedtechniques and the performance evaluations are presentedin the next section. Tables 3,4,5 and 6 illustrate theperformance evaluation of the proposed speech signalenhancement techniques for white, babble, mall and carnoise respectively (for SNR dB types -10 dB,-5 dB, 0dB,5 dB and 10 dB).Page 156
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.Table 3: Performance Evaluations of the Proposed Speech Signal Enhancement Techniques for White NoiseSNRdBTypes-10 dB-5 dB0 dB5 dB10 .604.983.24Volume 6, Issue 1, January – February 2017Page 157
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.Table 4: Performance Evaluations of the Proposed Speech Signal Enhancement Techniques for Babble NoiseSNRdBTypes-10 dB-5 dB0 dB5 dB10 035.013.022.803.36Volume 6, Issue 1, January – February 2017Page 158
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)Web Site: www.ijettcs.org Email: editor@ijettcs.orgVolume 6, Issue 1, January - February 2017ISSN 2278-6856.Table 5: Performance Evaluations of the Proposed Speech Signal Enhancement Techniques for Mall NoiseSNRdBTypesMetrics-5 dB0 dB5 dB10 ESQ-10 .09-1.76
speech signal enhancement such as spectral subtraction, adaptive filtering, Kalman filtering, wavelet filtering and Ideal Binary Mask (IBM) etc. The main objective of this paper is to implement efficient speech signal enhancement techniques which are suitable for different noisy conditions. The potent metrics of the
Speech enhancement based on deep neural network s SE-DNN: background DNN baseline and enhancement Noise-universal SE-DNN Zaragoza, 27/05/14 3 Speech Enhancement Enhancing Speech enhancement aims at improving the intelligibility and/or overall perceptual quality of degraded speech signals using audio signal processing techniques
main aim of speech enhancement is to enhance the quality and clarity of the speech signal. A number of techniques have been developed for providing better clarity speech signals which comprises of the techniques such as spectral subtraction [2], Wiener filtering [3] and Ephraim Malah filtering [4]. For the past two decades, speech enhancement has
that, the spectral subtraction algorithm improves speech quality but not speech intelligibility [2]. Consequently, in this research work, the most recent . namely, speech or speaker recognition, speech coding and speech signal enhancement. By using only a few wavelet coefficients, it is possible to obtain a
Keywords: Speech Enhancement, Spectral Subtraction, Kalman filter, Musical noise 1. INTRODUCTION Speech enhancement is used to improve intelligibility and overall perceptual quality of degraded speech using various algorithms and audio signal processing techniques. The aim of speech
speech enhancement based on the short-time spectral magnitude (STSM). In real processing speech enhancement techniques, the algorithm employed a simple principle in which the spectrum of the clean speech estimation signal can be obtained by subtracting a noise estimation spectrum from the noisy speech spectrum conditions.
Keywords: Speech Enhancement, Spectral Subtraction, Adaptive Wiener Filter . 1 INTRODUCTION. Speech enhancement is one of the most important topics in speech signal processing. Several techniques have been proposed for this purpose like the spectral subtraction approach, the signal subspace approach, adaptive noise canceling
Speech Enhancement Speech Recognition Speech UI Dialog 10s of 1000 hr speech 10s of 1,000 hr noise 10s of 1000 RIR NEVER TRAIN ON THE SAME DATA TWICE Massive . Spectral Subtraction: Waveforms. Deep Neural Networks for Speech Enhancement Direct Indirect Conventional Emulation Mirsamadi, Seyedmahdad, and Ivan Tashev. "Causal Speech
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