IJRIT International Journal of Research in Information Technology, Volume 1, Issue 9, September, 2013, Pg. 289-305

International Journal of Research in Information Technology (IJRIT)

www.ijrit.com

ISSN 2001-5569

INTELIGIBILITY IMPROVEMENT USING SNR ESTIMATION Durgesh1, Anil Garg 2, Pankaj Bactor 3, Shama Choudhary 4 1

M.Tech Student MMEC, Mullana, M.M. University, Mullana-Ambala-Haryana, India

2

Asst. Prof. in ECE department MMEC, Mullana, M.M. University, Mullana-Ambala-Haryana, India

3

Asst. Prof. in ECE department MMEC, Mullana, M.M. University, Mullana-Ambala-Haryana, India 4

1

M.Tech Student MMEC, Mullana

[email protected], [email protected]

Abstract This paper presented the comparison of spectral subtraction algorithm, minimum mean square error, wiener algorithm and TSDD algorithm, and performance evaluation of various modified decision-directed approach. This paper provides valuable hints for analyzing and optimizing noise-reduction algorithm. These techniques can help improve the quality and intelligibility of speech signals that have been deteriorated by noise. In this paper, the aim of speech enhancement algorithms is to improve the quality or intelligibility of the noisy speech signals by using different enhancement algorithms. Many speech enhancement algorithms are designed to suppress additive background noise. The speech enhancement methods aimed at suppressing the background noise are based on one way or the other on the estimation of the background noise.

Keywords: speech distortions, speech enhancement algorithm, and speech intelligibility improvement.

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 and Wiener filter. The aim of speech enhancement algorithm is to improve the quality of the speech signal [1]. These algorithms have been found to improve the speech quality [2]. From the all available speech Durgesh,IJRIT

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enhancement methods, the spectral subtraction technique is one of the first algorithms proposed for background noise reduction. This is done by subtracting the average magnitude of noise signal from the noisy speech to estimate the magnitude of the enhanced speech signal [3]. The Spectral-subtractive algorithms, is the simplest speech enhancement algorithms to implement. They are based on the principle that noise is additive and the noise spectrum can be estimated in the absence of speech and are subtracted from the noisy signal. Statistical model based algorithm, this algorithms, given a set of measurements that is, to the Fourier transform coefficients of the noisy signal, a linear or nonlinear estimator of the parameter of interest, namely the transform coefficients of the clean signal is found. The Wiener algorithm and minimum mean square error (MMSE) algorithms come in this Category. Subspace Algorithms, these algorithms are based on the principle the clean signals are not confined to the subspace of the noisy Euclidean space. Given a method of decomposing the vector space of the noisy signal into a subspace that is occupied primarily by the clean signal and a subspace that is occupied primarily by the noise signal, one could estimate the clean signal simply by nulling the component of the noisy vector residing in the noise subspace. These algorithms were evaluated using a developed noisy corpus best for evaluation of speech enhancement algorithm [4]. We have different type of distortion in speech enhancement algorithm can be divided into two categories: distortion that affects the speech signals itself, and second is distortion that affects the background noise. From these two types of distortion, listeners assume to determine the speech distortion when making knowledge of overall quality [5]. In the previous study, for overall quality and speech distortion, algorithm MMSE, log MMSE, wiener filter performed good in some condition. Subspace algorithms performed poorly for overall quality [6]. In this paper, we address on the subjective and objective comparison and evaluation of all above algorithms using different methods. The paper is organized as follows: section II gives overview of different methods or algorithms of speech enhancement, Section III presents the subjective and objective tests, and Section IV presents the performance of all above algorithms, and finally section IV present conclusions.

2. Speech Enhancement Methods There are various speech enhancement methods or algorithms proposed for noise reduction and to improve the noise quality and intelligibility. 2.1 Spectral Subtraction Algorithm Spectral subtraction is one of the first algorithms prefer for speech enhancement. It is simple and easy to implement, based on the principle that one can obtain an estimate of the clean signal spectrum by subtracting an estimate of the noise spectrum from the noisy speech spectrum. The noise spectrum can be estimated and updated, during the time interval when the signal is absent or when only noise is present. Assumption is noise is additive¸ its spectrum does not change with time means noise is stationary or it’s slowly time varying signal¸ whose spectrum does not change significantly between the updating periods [7] [4]. With this approach, estimate the enhanced speech spectrum is obtained by subtracting an estimate of the noise spectrum from the noisy speech spectrum during the period when the speech signal is not present. The key advantage of this method of speech enhancement is that it is simple and easy to implement. The spectral subtraction algorithm effectively reduces the noise which is present in the corrupted speech signal [7]. The principle of spectral subtraction algorithm is shown in Fig. 1. Let  be the noisy speech signal given by (1)  =  +  Where,  represents the clean speech signal and  is the uncorrelated additive noise. In spectral subtraction algorithm, it is assumed that the noise and clean signal are uncorrelated so as to estimate the noise spectrum. Initially, the spectral subtraction approach was used to estimate the short term magnitude spectrum of the clean  | from the noisy signal signal | |. This is done by subtracting the estimated noise magnitude spectrum | magnitude spectrum | |. The noisy signal phase spectrum is used as an estimate of the clean speech phase spectrum, as follows:  e,   = | | − 

(2)

Where, ,  is the phase of noisy signal  .The estimated time-domain clean speech signal is obtained by taking the inverse Fourier Transform of  . However, this approach has several shortcomings. Therefore, another enhanced version of spectral subtraction algorithm is proposed, the clean signal  is recovered from the noisy signal , Durgesh,IJRIT

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  , which is obtained by averaging over by assuming that there is an estimate of the power spectrum of noise  multiple frames of a known noise segment. An estimate of the short-time squared magnitude spectrum of the clean signal using this method can be obtained as follows:   , if | | −    ≥(3) | | −     ! 0, otherwise To recover the signal, the magnitude spectrum estimate is combined with the phase of the noisy signal as shown in Eqn. 4 and the Clean speech can be obtained with the Inverse Fourier Transform. (4)  =   e, 

Although the spectral subtraction algorithm can be easily implemented; yet, it has several shortcomings. The subtraction process needs to be done carefully to avoid any speech distortion. If too little is subtracted, much of the interfering noise remains¸ but if too much is subtracted, then some speech information might be removed [4]. 2.2 Wiener–type filtering algorithm This algorithm proposed for noise reduction. Principle lies to obtain an estimate of clean signal from that corrupted by additive noise [4]. This estimate is obtained by minimizing the mean square error between the desired signal and the estimated signal. In this the input signal goes through a LTI system to produce an output signal z (n). We design this in such a way that the output signal ,-  is close to desired signal d(n).This can be done by computing the estimation error, the optimal filter that minimize the estimation error is called the wiener filter. A linear discrete-time filter for estimating a desired signal z(n) based on an excitation x(n).We assume that both x(n) and d(n) are random processes. The filter output is ,-  and e (n) is the estimation error. Performance function is defined as (5) £=E [|.| ] Where e (n) = d (n)-z (n) The Wiener filter is used to reduce the amount of noise presented in a signal by comparison with an estimation of the desired noiseless signal. As can be observed in the results, the image restoration is not absolutely perfect but it achieves a very close image to the original one [4]. The goal of the Wiener filter is to filter out noise that has corrupted a signal. It is based on a statistical approach, and a more statistical account of the theory is given in the MMSE estimator article. 2.3 MMSE Estimator MMSE estimation is also known as Ephraim and Malah’s estimator [in 1984]. MMSE estimation producing colourless residual noise, this is the advantage of this method [8]. To overcome the problem of the musical noise distortion present in the above algorithm method, Ephraim and Malah, proposed the MMSE method which reduces the distracting musical noise to a considerable extent, and thus improved the quality of the resulting enhanced speech. Mainly MMSE based algorithms are Minimum Mean Square Error Short-Time Spectral Amplitude (MMSE-STSA) estimator and MMSE Logarithm Spectral Amplitude (MMSE-LSA) estimator. In various MMSE of power spectrum have been proposed. Some power spectrum estimator in decision-directed approach used for the calculation of a priori SNR [9]. In the previous, wiener method can derived by minimizing the error between a linear model of clean spectrum and real spectrum [4]. The MMSE-STSA method gives good results in reducing the musical noise; however, it suffers a drawback of not taking into consideration the non-linear characteristics observable in human perception. Therefore, MMSE-LSA enhancement method was proposed to minimize the mean square error between the logarithm of the STSA of the clean and enhanced speech. The MMSE-LSA is often favored because of its psychoacoustic considerations and provides a better quality of the enhanced speech. 2.4 TSDD ALGORITHM For improve the performance of a gain factor for noise reduction, a perceptual-decision directed approach, is proposed. Initially the decision-directed method (Ephraim and Malah), is performed to enhance a noisy and corrupted speech signal. Whereas, the decision-directed method is more suitable, to reduce the effect of musical residual noise. Therefore, a decision-directed method is performed again to improve the estimated a priori SNR by removing the frame relay. These procedures specify a two-step-decision directed approach algorithm. The drawback of decision-directed approach delay inherent in speech transients [11]. This delay version of gain factor will generate Durgesh,IJRIT

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a repetition. To compensate this we use the TSDD algorithm to improve the estimate of a priori SNR[10]. The gain factor of TSDD algorithm is given as: (6) g 677∗ 9:;<= 3,4 01223,45

>?677∗ 9:;<= 3,4

Where yABCD m, w is the posteriori SNR, and g 22 is the gain factor used to estimate a priori SNR. 3. Subjective And

Objective

3.1 Evaluation This review paper shows the previous result from the comparative analysis of the subjective and objective tests. Enhanced speech files were sent to Dynastat, Inc for subjective evaluation for evaluating noise suppression algorithm based on ITU-T p.835 [6]. [a] Subjective testing- include the methods which focused on speech intelligibility and overall quality. The subjective test include, the goodness test, Mean opinion score tests [MOS]. Another test which evaluates the speech and background signal quality across the multiple scales is diagnostic acceptability measure [10]. Subjective tests were arranged according to ITU-T P.835 methodology. In terms to assess perceived quality, a subjective mean opinion score test be performed, this test allows for overall quality. [i] Test methodology This method informs the listener to respectively attend to and rate the enhanced speech signal on [6]. A. SIG Speech signal alone using a five-point scale of signal distortion (Table 1). B. BAK Background noise alone is using a five-point scale of background intrusiveness (BAK) (Table 2). C.OVRL Overall effect using the scale of the (MOS) – [1= bad, 2= poor, 3= fair, 4= good, 5= excellent] [b] Objective test - depend on mathematically based measure between original and degraded speech. [i] Ltakura-saito measure: - The Itakura-Satio distance is a measure of the perceptual difference between an original spectrum and an approximation of that spectrum. The distortion measure is given by, LM P Q PR LM (7) ,FG HI, H∅  = K ∅MO K N ∅ NR O + STU V NMW − 1 LN

P∅ Q∅ P∅

L∅

Where YI is speech with linear prediction coefficient vector and Y∅ is processed speech coefficient vector which represents the all-pole gains for processed and clean speech. [ii] Log-Likelihood Ratio Measure: - Likelihood ratio test is used to compare the fit of two models one of which is nested within the other. The LLR measure is also referred to as takura distance. The LLR measure will be: (8) HI ]∅ HI^ ,ZZQ HI, H∅  = log \ ^_ H∅ ]∅ H∅ [iii] Segmental SNR Measure: - Correlation of SNR with subjective quality is not good. The time-domain segmental SNR measure was computed as: ,G`aGbQ

mi>

∑bh?bi> fg  10 j5bh = d STU bh?b c ∑j5bh kfI  − fg l h5n

(9)

Where fI  is the input signal, fg  is the enhanced signal, N is the frame length, and M is the number of frames in the signal. Only frames with SNRseg in the range of [-10, 35] dB were consider in the computation of the average [10].

4. Structure of speech enhancement algorithm Here we use the Statistical – model based algorithm (TSDD, WIENER, pMMSE, MMSE-SPU, SpecSub) for derive the magnitude spectra by minimizing the mean square error between the clean and estimated spectra. A positive difference between the clean and estimated spectra would signify attenuation distortion, while negative spectral

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difference would signify amplification distortion. The sub space techniques were design to minimize the mathematically derived speech distortion measures. [1] Figure shows the proposed model of speech enhancement algorithms. In this we takes the two speech signal one is clean signal and second one is noise signal, then we combine both the signal then the output will be noisy signal. [2] The speech signal is sampled at 25 kHz. The digitized speech is then partitioned into overlapping frames. We were applying the hanning window to the noisy signal with the 50% overlap. [3] Then take the Fast Fourier Transform to this signal. In order to apply the Fourier transform, it is necessary for the signal to be both stationary and infinite in length. For speech signals, both of these requirements are impractical. Speech signals are used to convey information and thus cannot be stationary by definition. It is also impractical for them to be infinite in length. Therefore, short-time analysis is used to make the Fourier transform of speech signals practical. [4] Speech enhancement algorithms that aim to restore the original speech signal by modifying the degraded signal in the frequency domain can typically be defined by their characteristic gain functions. [5] The enhanced speech frames are then synthesized using an inverse Fourier transform of the spectra. These frames are then overlapped and added to obtain the enhanced speech signal. Speech enhancement algorithm can improve the speech quality but not the speech intelligibility. Here we improve the intelligibility by focusing on the fine grain analysis of distortions introduced by speech enhancement algorithm. Here we consider the controlling of distortion properly to improve the intelligibility by increase the gain function and SNR values. [i] Proposed Model: -

Clean signal

Noise signal

Noisy signal or speech

Apply Hanning window w (n) Compute FFT . op Modify Noise spectrum Ŝ

Compute Gain Function

Synthesize Fourier Transform  . op Compute inverse FFT Fig1: Proposed Model of speech enhancement algorithm Durgesh,IJRIT

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Enhanced output

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[ii] INTELLIGIBILITY LISTENING TEST Estimate of the signal spectrum magnitude is obtain by multiplying Y(k,t) with a gain function G(k,t) as follows[12]  , r = s, r ∙ , r 10 The weiner gain function a priory SNR and is given by suvwjwx , ry

z{]|xv} , r 1 + z{]|xv} , r

11

z{]|xv} is the priori SNR estimated using the Decision Directed Approach:  , r − 1 z{]|xv} , r = ~ ∙ + 1 − ~ € , r − 1 12  , r ∙ H ‚ − 1,0ƒ € , r − 1 [iii] Signal to Noise Ratio It is an important feature in determining the quality of speech signal in the presence of noise [13]. Signal to noise ratio is defined as the power ratio between a signal (meaningful information) and the background noise (unwanted signal). The ratio is usually measured in decibels (dB) and can be expressed as …† z{]20STU10 V W …j

13

Unfortunately, in most applications the SNR cannot be easily derived as the noise energy is not usually known and it is difficult to identify between signal and noise if these are both have same frequency range.

5. Results [i] Fig.2. Shows the estimation of noisy spectrum. The gain function of TSDD algorithm, in terms of suppression, providing small attenuation and better intelligibility results in comparison to other algorithms.

Fig.2: Gain variation of TSDD algorithm [ii] Fig.3. Enhanced magnitude of priori SNR estimation.

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Fig.3: Enhanced magnitude of priori SNR estimation [iii] Fig.4 shows the gain variation of TSDD algorithm.

Fig.4: Gain variation of TSDD algorithm [iv] Fig.5 shows the new gain variation of TSDD algorithm, it is beneficial to obtain a lower speech distortion.

Fig.4: New Gain variation of TSDD algorithm These all results show the estimation of noisy spectrum and the new gain variation of TSDD algorithm using FFT.

6. Discussions The performance of spectral subtraction, MMSE, and wiener algorithm is good and also improve the quality of speech signal and TSDD gives the much better result than these entire algorithms it improves the quality as well as intelligibility of the signal. Noise estimation algorithms were assessed using both objective and subjective measures. The algorithms that performed the best in terms of low speech distortion were also the algorithms yielding the highest overall quality. This suggests that listeners were affected for the most part by the distortion imparted on the speech signal than on the background noise when making knowledge of overall quality. In the two step decisiondirected algorithm, the decision-directed algorithm is utilized to estimate the priori SNR. In turn, the estimated a priori SNR is refined again by the TSDD algorithm. The spectra of enhanced speech are obtained by multiplying the spectra of noisy speech with this perceptual gain factor.

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7. Conclusions This various approaches are used in this paper. The subjective evaluation, in terms of overall quality and speech distortion of speech, the algorithm performed best are: logMMSE, MMSE-SPU, pMMSE and MMSE-ne. Wiener algorithm also performed well in some cases. In the case of speech with weak energy, the values of gain factor for the perceptual and the proposed method are larger than those of the TSDD method. TSDD algorithm employs the Wiener filter twice to estimate the spectra of speech. Two step decision-directed approaches are better able to reduce greater amount of residual noise than the perceptual algorithm. Evaluation of TSDD algorithms revealed that these algorithms improve speech quality and also improve the intelligibility of speech signals. TSDD also improve the performance of perceptual method approach in removing maximum residual noise.

8. References [1] Hong-Yan Li, Qing Zhao, Guang-Ling Ren And Bao-Jin Xiao, “Speech Enhancement Algorithm Based On Independent Component Analysis”, ICNC, Vol.76, Pp.598-602, 2009. [2] Gibak Kim And Philipos C. Loizou, “Gain-Induced Speech Distortion And The Absence Of Intelligibility Benefit With Existing Noise-Reduction Algorithm”, J. Acoust. Soc. Am, Vol.103, Pp. 1501-1595, September 2011. [3] P. Krishnamoorthy And S.R. M. Prasanna, “Enhancement Of Noisy Speech By Temporal And Spectral Processing”, Sciencedirect Vol.53, Vol.154-174, 2011. [4] P.C. Loizou, “Speech Enhancement: Theory And Practice (CRC Press, Taylor Francis Group, Florida),” Pp. 1-394, 2007. [5] Yi Hu And C. Loizou, “Evaluation Of Objective Quality Measure For Speech Enhancement”, IEEE Trans. Audio And Language Processing, Vol.6, No.1, January 2008. [6] Yi Hu And Philipos C. Loizou, “Subjective Comparison And Evaluation Of Speech Enhancement Algorithm”, Sciencedirect, Speech Communication, Vol.49, Pp.588-601, 2007. [7] Yang Lu And Philipos C. Loizou, “A Geometric Approach To Spectral Subtraction”, Sciencedirect, Speech Communication,Vol 50,Pp.453-466, January 2008. [8] Teddy Surya Gunwan, Eliathamby Ambikairajah And Julien Epps, “Perceptual Speech Enhancement Exploiting Temporal Masking Properties Of Human Auditory System”, Sciencedirect, Speech Communicationvol.52, Pp.381-393, 2010. [9] Yang Lu And Philipos C. Loizou, “Estimator Of The Magnitude-Squared Spectrum And Method For Incorporating SNR Uncertainty”, IEEE Trans. Audio And Lang. Processing, Vol.19, No.5, Pp.1123-1137, 2011. [10] John H.L.Hansen And Bryan L. Pellom, “An Objective Quality Evaluation Protocol For Speech Enhancement Algorithm”, The International Conference On Speech And Language Processing Processing, 1998. [11] Ching-Ta Lu, “Enhancement Of Single Channel Speech Using Perceptual-Decision-Directed Approach”, Science Direct, Speech Communication, Vol.53, Pp.495-507, December 2010. [12] Philipos C. Loizou, Gibak Kim, “Reasons Why Current Speech Enhancement Algorithms Do Not Improve Speech Intelligibility And Suggested Solutions”, IEEE Transaction On Audio, Speech And Language Processing, Vol.19, No.1,Pp. 47-54, 2011. [13] Jianfen Ma, Philipos C. Loizou, “SNR Loss: A New Objective Measure For Predicting The Intelligibility Of Noise-Suppressed Speech”, Science Direct, Speech Communication, Vol.53, Pp.340-354, 2011.

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inteligibility improvement using snr estimation

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 and Wiener filter. The aim of speech enhancement algorithm is to improve the ...

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