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Single-Channel Speech Enhancement by NWNS and EMD
Somlal Das, Mohammad Ekramul Hamid, Keikichi Hirose, Md. Khademul Islam Molla
Pages - 279 - 291     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
speech enhancement, non linear weighted noise subtraction, empirical mode decomposition
This paper presents the problem of noise reduction from observed speech by means of improving quality and/or intelligibility of the speech using single-channel speech enhancement method. In this study, we propose two approaches for speech enhancement. One is based on traditional Fourier transform using the strategy of Noise Subtraction (NS) that is equivalent to Spectral Subtraction (SS) and the other is based on the Empirical Mode Decomposition (EMD) using the strategy of adaptive thresholding. First of all, the two different methods are implemented individually and observe that, both the methods are noise dependent and capable to enhance speech signal to a certain limit. Moreover, traditional NS generates unwanted residual noise as well. We implement nonlinear weight to eliminate this effect and propose Nonlinear Weighted Noise Subtraction (NWNS) method. In first stage, we estimate the noise and then calculate the Degree Of Noise (DON1) from the ratio of the estimated noise power to the observed speech power in frame basis for different input Signal-to-Noise-Ratio (SNR) of the given speech signal. The noise is not accurately estimated using Minima Value Sequence (MVS). So the noise estimation accuracy is improved by adopting DON1 into MVS. The first stage performs well for wideband stationary noises and performed well over wide range of SNRs. Most of the real world noise is narrowband non-stationary and EMD is a powerful tool for analyzing non-linear and non-stationary signals like speech. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode function (IMFs). Since the IMFs having different noise and speech energy distribution, hence each IMF has a different noise and speech variance. These variances change for different IMFs. Therefore an adaptive threshold function is used, which is changed with newly computed variances for each IMF. In the adaptive threshold function, adaptation factor is the ratio of the square root of added noise variance to the square root of estimated noise variance. It is experimentally observed that the better speech enhancement performance is achieved for optimum adaptation factor. We tested the speech enhancement performance using only EMD based adaptive thresholding method and obtained the outcome only up to a certain limit. Therefore, further enhancement from the individual one, we propose two-stage processing technique, NWNS+EMD. The first stage is used as a pre-process for noise removal to a certain level resulting first enhanced speech and placed this into second stage for further removal of remaining noise as well as musical noise to obtain final enhancement of the speech. But traditional NS in the first stage produces better output SNR up to 10 dB input SNR. Furthermore, there are musical noise and distortion presented in the enhanced speech based on spectrograms and waveforms analysis and also from informal listening test. We use white, pink and high frequency channel noises in order to show the performance of the proposed NWNS+EMD algorithm.
CITED BY (2)  
1 Islam Molla, M. K., Das, S., Hamid, M. E., & Hirose, K. (2013). Empirical Mode Decomposition for Advanced Speech Signal Processing. Journal of Signal Processing, 17(6), 215-229.
2 Hamid, M. E., Das, S., Hirose, K., & Molla, M. K. I. (2012). Speech enhancement using EMD based adaptive soft-thresholding (EMD-ADT). International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(2), 1-16.
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Mr. Somlal Das
Rajshahi University - Bangladesh
Dr. Mohammad Ekramul Hamid
University of Rajshahi - Bangladesh
Dr. Keikichi Hirose
University of Tokyo - Japan
Dr. Md. Khademul Islam Molla
University of Tokyo - Japan

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