Speech enhancement for non-stationary noise environment by adaptivewavelet packet
ABSTRACT We consider the non-stationary or colored noise estimation by wavelet thresholding method. First, we propose node dependent thresholding for adaptation in colored or non-stationary noise. Next, we suggest a noise estimation method based on spectral entropy using histogram of intensity instead of estimation method based on median absolute deviation (MAD). We use a modified hard thresholding to alleviate time-frequency discontinuities. The proposed methods are evaluated on various noise conditions - white Gaussian noise, car interior noise, F-16 cockpit noise, pink noise, speech babble noise. We compare our proposed methods with the conventional one with level dependent thresholding based on MAD
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ABSTRACT: Performance of wavelet thresholding methods for speech enhancement strongly depends on estimating an exact threshold value in the wavelet sub-bands. In this paper, we propose a new method for more exact estimating of the threshold value. Our proposed threshold value is firstly obtained based on the symmetric Kullback-Leibler divergence between the probability distributions of noisy speech and noise wavelet coefficients. In the next step, we improved this value using the segmental Signal-to-Noise Ratio (SNR). We used some TIMIT utterances to assess the performance of the proposed threshold. The algorithm is evaluated using the Perceptual Evaluation of Speech Quality (PESQ) score and the SNR improvement in ideal and real modes. In ideal and real modes, on average, we obtain respectively 2.25 dB and 1 dB SNR improvement and a PESQ score increase up to 1.1, 0.75 compared with the conventional wavelet thresholding approaches. In comparison to the adaptive thresholding approach, on average in ideal and real modes, we obtain respectively 1.6 dB and 0.9 dB SNR improvement. The PESQ value of the adaptive thresholding method, in the real and ideal modes, is 0.25 higher and 0.5 lower than that of our proposed method, respectively.Signal Processing 07/2014; · 2.24 Impact Factor
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ABSTRACT: In this work, we propose a new approach to improve the performance of speech enhancement technique based on partial differential equations. As we know, the real-world noise is highly random in nature. So we try for reduction of white Gaussian noise. The proposed method was evaluated on several speakers. The subjective and objective results show that the new method highly improves speech enhancement. Comparisons of several methods are reported.Australian Journal of Basic and Applied Sciences. 01/2010; 4(7):2093-2098.
Article: Knowledge-Based Speech Enhancement