Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis

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1 Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins College of Engineering for Women, Pune Abstract This paper presents a new approach to speech signal Enhancement in highly Non-Stationary Environment. Noise Spectrum estimation is fundamental and essential component of speech enhancement system. The proposed technique is based on Independent Component Analysis for Blind Source Separation along with Spectral subtraction method which enhances signal. An Independent Component Analysis is phenomenon which separates various statistically independent components of input speech signal from an observed mixture vector. An Independent component analysis extracts clean speech signal and noise signal from a mixture of individual sources. Improved Minima controlled Recursive averaging (IMCRA) technique estimates noise spectrum based on speech presence probability, past power spectral values, time and frequency dependent smoothing parameter. The well known spectral subtraction method subtracts estimated noise spectrum from a spectrum of noisy speech yields to produce an enhanced speech. The Time as well as frequency domain Objective Quality Measures such as Log-Likelihood Ratio (LLR), Frequency weighted segmental SNR (fwsnrseg), Weighted Spectral Slope (WSS), Perceptual Evaluation of speech Quality (PESQ), Itakura-Saito (IS) Ratio are then evaluated for resultant Enhanced speech signal with respect to the original desired signal. Keywords Independent Component Analysis, Improved Minima Controlled Recursive Averaging, Spectral Subtraction, Objective Quality Measures. I. INTRODUCTION Noise power spectrum estimation is a fundamental and most crucial aspect of speech enhancement as well as speech recognition systems. The efficiency of such systems, particularly under low input signal-to-noise ratio (SNR) conditions and non-stationary noise environments, is adversely affected by the capability to effectively track fast variations in the statistics of the noise. A well known approach to estimate noise power spectrum is to average the noisy signal over non speech sections. The traditional voice activity detectors (VAD) based on SNR are difficult to tune and their application to low input SNR speech results often yields to distorted speech [9]. Martin [3] has proposed a new algorithm for noise estimation based on minimum statistics. The noise estimate is obtained as the minima values of a smoothed power estimate of the noisy signal, multiplied by a bias compensation factor that compensates the bias effect. However, this noise estimate is sensitive to outliers, furthermore this may occasionally attenuate low energy speech components; particularly if the minimum search window is too short [3]. These limitations can be overcome, at the cost of higher complexity, by adapting the time as well as frequency dependent smoothing parameter and the bias compensation factor [4]. Recently, Cohen introduced a noise estimation approach, namely Minima Controlled Recursive Averaging (MCRA) [8] that combines the robustness of the minimum tracking algorithm with the simplicity of the recursive averaging. The noise estimate is obtained by averaging past spectral power values, using a smoothing parameter that is adjusted by the speech presence probability in sub bands [5][6]. Cohen further improves the MCRA estimator with regard to the following aspects: Minimum tracking during speech presence frame, speech presence probability estimation, and derivation of frequency and time dependent bias compensation factor. We have combined Kurtosis method of independent component analysis [1] to solve blind source separation problem along with improved minima controlled recursive averaging (MCRA) noise spectrum estimation technique in order to improve robustness and performance of speech enhancement algorithm. An estimated noise power spectrum is then eliminated using normal spectral subtraction process [3], it tends to produce an enhanced speech signal to the great extent. 76

2 Paper elaborates a speech enhancement technique based on Independent Component Analysis and improved minima controlled recursive averaging noise estimation method for white noise as well as highly Non-stationary Noisy environment. The rest of the paper is organized as follows. Section II describes the Independent component analysis. Section III describes improved minima controlled recursive averaging. Section IV describes Proposed System. Section V describes the Simulation Results. II. INDEPENDENT COMPONENT ANALYSIS Independent component analysis (ICA) is a highly effective mechanism for numerous applications such as blind source separation (BSS), unsupervised learning, feature extraction, and data compression. ICA is related to principal component analysis (PCA) and factor analysis (FA) in which the former estimates a leastsquares linear transformation and extracts the uncorrelated components. However, ICA finds a set of latent components that are non-gaussian and mutually independent [2].Independent component analysis was originally developed to deal with problems that are closely related to the cocktail-party problem. Since the recent increase of interest in ICA, it has become clear that this principle has a lot of other interesting applications as well. More realistic and general ICA model is the noising case: (1) x(t) = As(t) v(t) The basic problem of ICA is then to estimate the realizations of the original using only observation of the mixture x(t), let us denote W, and obtain the independent component simply by S (t) =Wx(t) (2) The model of noisy ICA can be depicted by Fig. A. Preprocessing For ICA: Fig. 1: ICA Model ICA is performed on multidimensional data. This data may be corrupted by noise, and several original dimensions of data may contain only noise. So if ICA is performed on a high dimensional data, it may lead to poor results due to the fact that such data contain very few latent components. Hence, reduction of the dimensionality of the data is a preprocessing technique that is carried prior to ICA. 1) Centering: It is easier to estimate an Un-mixing Matrix W if the measured signals have a mean of zero, a variance of one and zero correlation. That is then we obtain the centered observation vector, Xc, as follows: Xc= X- m (3) This step simplifies ICA algorithms by allowing us to assume a zero mean. 2) Whitening: Whitening thus reduces the number of parameters to be estimated. Instead of having to estimate the n 2 elements of the original matrix A, we only need to estimate the new orthogonal mixing matrix, where an orthogonal matrix has n (n 1)/2 degrees of freedom. This procedure is also called sphereing since it normalizes the Eigen values of the covariance matrix. X (t) = Ux(t) (4) Where the whitening matrix U is usually computed after singular or Eigen-value decomposition of the covariance matrix of x(t): U= VD -0.5 VT (5) Where V is the orthogonal matrix composed eigenvector of covariance of x and D=diag(d1,,dn) is the diagonal matrix of its Eigen values. Then the aim of ICA is to estimate the separation matrix W, and the separation signal, y(t) = WX (t) (6) is the estimation of source signal. B. The FASTICA Algorithm: The FASTICA proposed by Hyvanrinen is based on a fixed-point iteration scheme. Here we adopted kurtosis as the estimation rule of independence. Kurtosis has widely used as a measure of non-gaussianity in ICA and related fields, which can be estimated simply by using the fourth moment of the sample data. Kurtosis is defined as follows: We erect adjective function: (7) (8) Since the observation signal has been pre-whitening, thus equation (8) can be simplified as: 77

3 (9) Seeking the gradient of equation (9), we get the following: (10) Using the fixed point algorithm, the iteration of fixed point algorithm can be expressed: (11) Thus we obtain the FASTICA algorithm as follows: (1) Center the data to make its mean zero. (2) Whiten the data to get x (t) (3) Make i=1; (4) Choose an initial orthogonal matrix foe W and make k=1; (5) Make Where, k= Frequency Bin index l = Frame Index 1) Initialize variables at the first frame for all frequency bins k. 2) For all time frames l For all frequency bins k, Compute posteriori SNR and Conditional gain as follows: Posteriori is defined by (13) Where, denotes short term spectrum of speech and noise signal. Priori SNR is estimated by, (6) Make (7) If not converged, make k=k+1 and go back to step (5) (8) Make i=i+1 (9) When i<number of original signals, go back to step (4) Until wi(k) T wi(k-1) is equal or close to 1, the iteration finish. it is essential to estimate noise level for each individual node in tree for effective noise cancellation. III. IMPROVED MINIMA CONTROLLED RECURSIVE AVERAGING The performance of basic MCRA algorithm is improved based on some additional aspects as follows: Speech presence and absence probability estimation, Minimum tracking during only speech activity period which are fundamental techniques introduced with procedure of two iterations for smoothing of noisy power spectrum and minimum tracking. First iteration accomplishes voice activity detection in each frequency band. The smoothing procedure in second iteration provides relatively strong speech components; this facilitates decreased variance of the minima values. Let x (n) and d (n) denotes speech and uncorrelated additive noise signals, respectively. The observed noisy signal y (n) is divided into overlapping frames and it is analyzed using the short-time Fourier transform (STFT) (12) (14) Where, α is weighting Factor which controls tradeoff between signal distortion and noise reduction. (15) is the spectral gain function of the Log- Spectral Amplitude (LSA) estimator in a case when speech is present. 3) Compute first iteration of smoothed power spectrum in time domain. (16) Where, is frequency smoothing of noisy power spectrum in each frame. b(i) (17) is normalized window function over period. Next task is to update minimum value of following equation: by 78

4 (18) parameters for Non stationary Babble noise corrupted speech samples. 4) Compute the indicator function ( ) for speech presence time period. 5) Calculate Speech presence probability (19) (20) Is speech absence probability. 6) Update Noise spectrum estimate, Fig.2 Enhancement of speech signal under influence of additive white Gaussian Noise (20dB) (a) Spectrogram of Clean speech (b) Noisy speech (c) enhanced speech (21) Where, is a time varying frequency dependent smoothing parameter. IV. SIMULATION RESULTS In the Simulation, the test speech signals are taken from NOIZEUS database. Performance of proposed technique is evaluated with respect to additive white Gaussian Noise as well as various Non stationary noise environments.figure1 indicates spectrogram plot at various stages of proposed system in a particular case of additive white Gaussian noise mixed with clean speech signal (sp01.wav) at 20 db Signal to noise ratio. Figure2 corresponds to spectrogram plot in case of enhancement stages of speech signal corrupted by babble noise at 15 db signal to noise ratio. Fig3 clearly interprets that proposed system works very effectively when input signal SNR is low. Characteristics of enhanced speech signal are evaluated based on various objective measures [7] of speech under influence of additive white Gaussian and various non stationary noise signals. We have measured Log-Likelihood ratio, Frequency weighted Segmental SNR, Weighted spectral slope, Itakura Saito Ratio and perceptual evaluation quality parameters for a speech signal which was initially corrupted by Noise at different db values. Following tables indicate that, denoised speech signal has enhanced objective parameters for white noise corrupted signal at low input SNR as compared with evaluation Fig.3 Enhancement of speech signal under influence of Babble Noise (15dB) (a) Spectrogram of Clean speech (b) Noisy speech (c) enhanced speech Fig.4 Enhancement of speech signal under influence of Babble Noise (0dB) (a) Spetrogram of Clean speech (b) Noisy speech (c) enhanced speech 79

5 Table I. Evaluation parameters of enhanced speech signal with respect to initial noisy speech parameters.(awgn Noise) Table II. Evaluation parameters of enhanced speech signal with respect to initial noisy speech parameters.(babble Noise) V. CONCLUSION In a case of non-stationary noise and additive white Gaussian noise environments, the IMCRA approach is very effective. When it is integrated with speech enhancement algorithms such as independent component analysis, achieves better speech quality with reduced residual noise, as compared to other competitive noise estimation methods. Efficiency of IMCRA method is evaluated based on Objective and subjective evaluation parameters under various environmental conditions. We examine, improvement in frequency weighted signal to noise ratio, Itakura Saito ratio with respect to their corresponding input signal to noise ratio. We conclude that the improved minima controlled recursive averaging noise estimate is superior, specifically, it responses more quickly to noise variations. It enhances signal parameters noticeably in following specific cases: low input SNR, weak speech components and in robust non stationary noise environment. VI. REFERENCES [1] Li Hongyan, Ren Guanglong, Blind separation of noisy mixed speech signals based Independent Component Analysis, 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, / IEEE. [2] Qingyun Wang, Hui Zong. Speech extraction method based on multiple reference signals ICA algorithm, 2011 International Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), /11/ 2011 IEEE [3] R. Martin, Spectral subtraction based on minimum statistics," Proc. 7th European Signal Processing Conf., EUSIPCO-94, Edinburgh, Scotland, September 1994, pp [4] Rainer Martin Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics, IEEE transactions on speech and audio processing, vol. 9, no. 5, July 2001, / IEEE. [5] Israel Cohen, Noise Estimation by Minima Controlled Recursive Averaging for Robust Speech Enhancement, IEEE signal processing letters, vol. 9, No. 1, January 2002, / IEEE. [6] Israel Cohen Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging, IEEE transactions on speech and audio processing, vol. xx, no. y, month 2003, [7] Yi Hu and Philipos C. Loizou, Senior Member, IEEE, Evaluation of Objective Quality Measures for Speech Enhancement, IEEE transactions on audio, speech, and language processing, vol. 16, no. 1, January 2008, / 2007 IEEE. [8] I. Cohen and B. Berdugo, Spectral enhancement by tracking speech presence probability in subbands," Proc. IEEE Workshop on Hands Free Speech Communication, HSC'01, Kyoto, Japan, 9-11 April 2001, pp [9] H. G. Hirsch and C. Ehrlicher, Noise estimation techniques for robust speech recognition, Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, pp ,

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