This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement method

Muni Kumar, T.

Rama Murthy, M. B.

Rama Rao, Ch. V.

Rao, K Srinivasa

English

Interscience Research Network

International Journal of Electronics Signals and Systems Volume 1 Issue 3 Article 12 January 2012 A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS T. Muni Kumar Gudlavalleru Engineering College, Gudlavalleru-521356, AP, India, munikumar.thottempudi@gmail.com M. B. Rama Murthy CMR College of Engineering&Technology, Kandlakoya, Hyderabad-509001, AP, India., mbrmurthy@gmail.com Ch. V. Rama Rao Gudlavalleru Engineering College, Gudlavalleru-521356, AP, India., chvramaraogec@gmail.com K Srinivasa Rao TRR College of Engineering, Pathancheru-502319, AP, India., principaltrr@gmail.com Follow this and additional works at: https://www.interscience.in/ijess  Part of the Electrical and Electronics Commons Recommended Citation Muni Kumar, T.; Rama Murthy, M. B.; Rama Rao, Ch. V.; and Rao, K Srinivasa (2012) "A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS," International Journal of Electronics Signals and Systems: Vol. 1 : Iss. 3 , Article 12. DOI: 10.47893/IJESS.2012.1040 Available at: https://www.interscience.in/ijess/vol1/iss3/12 This Article is brought to you for free and open access by the Interscience Journals at Interscience Research Network. It has been accepted for inclusion in International Journal of Electronics Signals and Systems by an authorized editor of Interscience Research Network. For more information, please contact sritampatnaik@gmail.com.  _________________________________________________________________________________________________________________ A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS T. Muni Kumar , M.B.Rama Murthy , Ch.V.Rama Rao , K.Srinivasa Rao                                        Gudlavalleru Engineering College, Gudlavalleru-521356, AP, India CMR College of Engineering&Technology,  Kandlakoya, Hyderabad-509001, AP, India.   Gudlavalleru Engineering College, Gudlavalleru-521356, AP, India. TRR College of Engineering, Pathancheru-502319, AP, India. E-mail : munikumar.thottempudi@gmail.com , mbrmurthy@gmail.com , chvramaraogec@gmail.com\                                                                             principaltrr@gmail.com Abstract- This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement methods   1. INTRODUCTION The objective of speech enhancement process is to improve the quality and intelligibility of speech in noisy environments. The problem has been widely discussed over the years. Many approaches have been proposed like subtractive type [1-4], Perceptual Wiener filtering algorithms. Among them spectral subtraction and the Wiener filtering algorithms are widely used because of their low computational complexity and impressive performance.  In these algorithms, Such methods return residual noise known as musical noise. This type of noise is quite annoying. In order to reduce the effect of musical noise, several solutions have been proposed. Some     involve adjusting parameters of spectral subtraction so as to offer more flexibility as in [2] and [3]. Other such as proposed in [4], are based on signal subspace approaches. Despite the effectiveness of these techniques to improve the signal to noise ratio (SNR), the problem of eliminating or reducing musical noise is still a challenge to many researchers. In the last few decades the introduction of psychoacoustic models has attracted a great deal of interest. The objective is to improve the perceptual quality of the enhanced signal. In [3], a psychoacoustic model is used to control the parameters of the spectral subtraction in order to find the best trade of between noise reduction and speech distortion.       To make musical noise inaudible, the linear estimator proposed in [5] incorporates the masking properties of the human auditory system. In [6], the masking threshold and intermediate signal, which is slightly denoised and free of musical noise, are used to detect musical tones generated by the spectral subtraction methods. This detection can be used by a post-processing aimed at reducing the detected tones. These perceptual speech enhancement systems reduce the musical noise but introduce some undesired distortion to the enhanced speech signal. When this distorted estimated speech signal is applied to the recognition systems their performance degrades drastically.  The basic idea of the proposed method is to remove, perceptually significant noise components from the noisy signal, so that the clean speech components are not affected by processing. In addition, the technique requires very little a priori information of the features of the noise. In the present paper, we propose to control the perceptual wiener filtering by psychoacoustically motivated filter that can be regarded as weighting factor. The purpose is to minimize the perception of musical noise without degrading the clarity of the enhanced speech. International Journal of Electronic Signals and Systems212   A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS ______________________________________________________________________________________________________________  _______________________________________________________________________________________________________________  2. STANDARD SPEECH ENHANCEMENT TECHNIQUE Let the noisy signal can be expressed as       )()()( ndnsny +=  ,                         (1) Where )(nx  is the original clean speech signal and )(nd is the additive random noise signal, uncorrelated with the original signal. Taking DFT to the observed signal gives       ),(),(),( kmDkmSkmY += .                  (2) Where Mm ,...,2,1=  is the frame index,  Kk ,....,2,1=  is the frequency bin index, M is the total number of frames and K  is the frame length, ),(),,( kmSkmY and ),( kmD represent the short time spectral components of the )()(),( nandnSny , respectively. Clean speech spectrum ),(ˆ kmS is obtained by multiplying noisy speech spectrum with filter gain function as given in eqation (3)      ),(),(),(ˆ kmYkmHkmS =                          (3) Where ),( kmH  is the noise suppression filter gain function (conventional Wiener filter (WF)), which is derived according to MMSE estimator and ),( kmH is given by ),(1),(),(kmkmkmHξξ+=                    (4) Where ),( kmξ is an apriori SNR, which is defined as ),(),(),(kmkmkmdsΓΓ=ξ . (5)    { }2),(),( kmDEkmd =Γ and   { }2),(),( kmSEkms =Γ   represents the estimated noise power spectrum and clean speech power spectrum, respectively. A posteriori estimation is given by   ),(),(),(2kmkmYkmdΓ=γ                                         (6)   An estimate of ),(ˆ kmξ of ),( kmξ is given by the well known decision directed approach [9] and is expressed as  [ ].,(')1(),1(),1(),(̂2kmVPkmYkmHkmdααξ −+Γ−−=       (7) Where ,1),(),( −= kmkmV γ   [ ] xxP =   if 0≥x  and [ ] 0=xP  otherwise. The noise suppression gain function is chosen as the Wiener filter similar to [13]  3. PERCEPTUAL SPEECH ENHANCEMENT Although the Wiener filtering reduces the level of musical noise, it does not eliminate it [15]. Musical noise exists and perceptually annoying.  In an effort to make the residual noise perceptually inaudible, many perceptual speech enhancement methods have been proposed which incorporates the auditory masking properties [2-9]. In these methods residual noise is shaped according to an estimate of the signal masking threshold [9, 13]. Figure 1 depicts the complete block diagram of the proposed speech enhancement method. International Journal of Electronic Signals and Systems213   A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS ______________________________________________________________________________________________________________  _______________________________________________________________________________________________________________   Figure1. Block diagram of the proposed speech enhancement method 3.1 Gain of Perceptual Wiener filter (PWF) The perceptual Wiener filter (PWF) gain function ),(1 kmH  is calculated based cost function, J  which is defined as ⎥⎦⎤⎢⎣⎡ −=2),(),(ˆ kmSkmSJ            (8) Substituting   (2) and (3) in (9) results to             { }211 ),(),(),()1),(( kmDkmHkmSkmHE +−=             ii rd +=                                                    (9) Where  [ ]221 ),()1),(( kmSEkmHdi −=  and [ ]221 ),(),( kmDEkmHri =  represents speech distortion energy and residual noise energy. To make this residual noise inaudible, the residual noise should be less than the auditory masking threshold, ),( kmT .  This constraint is given by         ir   ≤  ),( kmT       (10) By including the above constraint and substituting { }2),(),( kmDEkmd =Γ and { }2),(),( kmSEkms =Γ   in (9) the cost function will become as ( )[ ]{ }0,),(),(max),(),()1),(( 2121 kmTkmkmHkmkmHJ ds −Γ+Γ−=  (11) The desired perceptual modification of Wiener is obtained by differentiating J w.r.t  ),(1 kmH and equating to zero. The obtained perceptually defined Wiener filter gain function is given by )0),,(),(max(),(),(),(1 kmTkmkmkmkmHdss−Γ+ΓΓ=  (12) By multiplying and dividing equation (12) with ),( kmdΓ , ),(1 kmH will become as ),()0),,(),(max(),(̂),(̂),(1kmkmTkmkmkmkmHddΓ−Γ+=ξξ     (13) ),( kmT is noise masking threshold which is estimated based on[16] noisy speech spectrum. A priori SNR and noise power spectrum were estimated using the two -step a priori SNR estimator proposed in [15] and weighted noise estimation method proposed in[17],respectively. 3.2 WEIGHTED PWF Although perceptual speech enhancement methods perform better than the non-perceptual methods, most of them still return annoying residual musical noise. Enhanced speech signal obtained using above mentioned perceptual Wiener filter still contains some residual noise due to the fact that only noise above the noise masking threshold is filtered and noise below the noise masking threshold is remain. It can affect the performance of perceptual speech enhancement method that processes audible noise only.   In order to overcome this drawback we propose to weight the perceptual Wiener filters using a psychoacoustically motivated weighting filter. Psychoacoustically motivated weighting filter is given by  Windowing +      FFT Amplitude Estimation of NMT Noise estimation PWF WPWF **IFFT-Overlap-Add  Phase signalNoisysignalEnhancedATHInternational Journal of Electronic Signals and Systems214   A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS ______________________________________________________________________________________________________________  _______________________________________________________________________________________________________________ ⎩⎨⎧ ≤Γ<=otherwisekmTkmifATHkmHkmW d,1),(),(),,(),(      (15) Where ),( kmATH  is the absolute threshold of hearing. This weighting factor is used to weight the perceptual wiener filter. The gain function of the ),(2 kmH of the proposed weighted perceptual Wiener filter is given by  ),(),(12 kmWkmHH =    (16)   5. SIMULATION RESULTS To evaluate and compare the performance of the proposed scheme of speech enhancement, simulations are carried out with the NOIZEUS, A noisy speech corpus for evaluation of speech enhancement algorithms, database [18]. The noisy database contains 30 IEEE sentences (produced by three male and three female speakers) corrupted by eight different real world noises at different SNRs. Speech signals were degraded with different types of noise at global SNR levels of 0 dB, 5 dB, 10 dB and 15 dB. In this evaluation only five noises are considered those are babble, car, train, airport and street noise. The objective quality measures used for the evaluation of the proposed speech enhancement method are the segmental SNR and PESQ measures[19]. It is well known that the segmental SNR is more accurate in indicating the speech distortion than the overall SNR. The higher value of the segmental SNR indicates the weaker speech distortion. The higher PESQ score indicates better perceived quality of the proposed signal [19].  The performance of the proposed method is compared with Wiener filter and perceptual Wiener filter.  The simulation results are summarized in Table 1 and Table 2. The proposed method leads to better denoising quality for temporal and the better improvements are obtained for the high noise level.  The time-frequency distribution of speech signals provides more accurate information about the residual noise and speech distortion than the corresponding time domain wave forms. we compared the  spectrograms for each of the method and confirmed a reduction of the residual noise and speech distortion. Figure2. Represents the  spectrograms of the clean speech signal, noisy signal and enhanced speech signals.  Table.1 Segmental SNR values of Enhanced Signals  Noise Type Input SNR WF PWF Proposed method (dB)  Babble  0 -4.59 -0.61 0.22 5 -1.39 0.01 0.32 10 0.02 0.65 2.14 15 0.75 2.71 3.97  Car  0 -3.93 -0.24 0.85 5 -1.65 0.52 1.20 10 0.69 0.70 2.37 15 0.72 2.31 3.81  Train  0 -3.45 -0.49 0.15 5 -0.86 0.38 0.43 10 -0.39 0.77 2.20 15 0.75 2.62 3.5  Airport 0 -4.37 -0.24 0.19 5 -2.57 0.15 0.43 10 -0.06 0.14 1.09 15 0.75 1.88 3.65  Street 0 -2.88 -0.15 0.08 5 -2.13 0.61 0.73 10 0.69 1.20 2.70 15 0.77 2.25 3.42  Table.2 PESQ values of the enhanced signals  Noise Type Input SNR (dB) WF PWF Proposed method   Babble  0 1.221 0.952 1.427 5 1.728 1.750 1.836 10 2.034 2.276 2.402 15 2.127 2.609 2.718  Car  0 1.165 1.439 1.734 5 1.694 1.697 2.107 10 1.921 2.168 2.318 15 2.265 2.645 3.127  Train 0 1.450 1.482 1.731 5 1.680 1.715 2.133 10 2.009 2.096 2.479 15 2.040 2.032 2.714  Airport 0 1.472 1.561 1.759 5 1.492 1.769 2.242 10 2.025 2.413 2.538 15 2.249 2.579 2.715 Street 0 1.636 1.782 1.817 5 1.679 1.857 1.968 10 2.119 2.260 2.392 15 2.380 2.573 2.683    6. CONCLUSION  In this paper, an effective approach for suppressing musical noise presented after wiener filtering has been introduced. Based on the perceptual properties of the human auditory system, a weighting factor accentuates the denoising process when noise is International Journal of Electronic Signals and Systems215   A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS ______________________________________________________________________________________________________________  _______________________________________________________________________________________________________________ perceptually insignificant and prevents that residual noise components might become audible in the absence of adjacent maskers. When the speech signal is additively corrupted by babble noise and car noise objective measure results showed the improvement brought by the proposed method in comparison to some recent filtering techniques of the same type.    Figure2. speech spectrogram,(a)original clean signal,(b) noisy signal(babble noise SNR=5dB),(c)enhanced signal using Wiener filter(d)enhanced signal using PWF,(e)enhanced signal using Weighted PWF 7. REFERENCES  [1] Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short-time spectral amplitude   estimator,” IEEE Trans. Acoust., Speech, Signal Processing,vol. ASSP-32, pp. 1109–1121, Dec 1984. [2] R. Schwartz M. Berouti and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” Proc. of ICASSP,   1979, vol. I, pp. 208–211. [3] N.Virag, “Single channel speech enhancement based on masking properties of the human auditory system,” IEEE   Trans. Speech and Audio Processing, vol. 7, pp. 126–137, 1999. [4] Y. Ephraim and H.L. Van Trees, “A signal subspace approach for speech enhancement,” IEEE Trans. Speech and Audio Processing, vol. 3, pp. 251–266, 1995. [5] Y. Hu and P. 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International Journal of Electronic Signals and Systems216   A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS ______________________________________________________________________________________________________________  _______________________________________________________________________________________________________________ [18] http://www.utdallas.edu/~loizou/speech/noizeus/ [19] Yi Hu and Philipos C. Loizou, “Evaluation of Objective Quality Measures for Speech   Enhancement,” IEEE Trans. on Audio, Speech and Language Processing, vol. 16, no. 1, pp. 229-        238, January 2008.    International Journal of Electronic Signals and Systems217

A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS

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A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS

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