A Conceptual Framework for Noise Reduction by Jacob Benesty, Jingdong Chen

By Jacob Benesty, Jingdong Chen

Though noise aid and speech enhancement difficulties were studied for no less than 5 many years, advances in our figuring out and the advance of trustworthy algorithms are extra very important than ever, as they help the layout of adapted ideas for in actual fact outlined functions. during this paintings, the authors suggest a conceptual framework that may be utilized to the numerous diverse features of noise aid, delivering a uniform process
to monaural and binaural noise relief difficulties, within the time area and within the frequency area, and regarding a unmarried or a number of microphones. in addition, the derivation of optimum filters is simplified, as are the functionality measures used for his or her evaluation.

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A Conceptual Framework for Noise Reduction

Although noise relief and speech enhancement difficulties were studied for a minimum of 5 many years, advances in our figuring out and the improvement of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted strategies for truly outlined functions. during this paintings, the authors suggest a conceptual framework that may be utilized to the numerous diverse facets of noise relief, providing a uniform method of monaural and binaural noise aid difficulties, within the time area and within the frequency area, and concerning a unmarried or a number of microphones.

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So, the larger the filter length, the more intelligible is the enhanced speech with the Wiener filter. In comparison, the quality index first decreases and then increases with L, which means that the quality of the enhanced signal with the Wiener filter is not a monotonic function of L. The quality first increases and then decreases as the filter length increases. The output SNR is seen to increase with L for the studied range of filter length; but it first increases quickly and then starts to saturate when L is large.

Low values of the speech quality indices imply a good signal quality. The global speech intelligibility index quantifies the amount of the desired signal that is affected by the filter. The subband and fullband global speech intelligibility indices are derived from the previous definitions: υi [h(k, n)] = (1 − ) υi [h(k, n)] + υq [h(k, n)] , k = 0, 1, . . 50) and υi [h(:, n)] = (1 − ) υi [h(:, n)] + υq [h(:, n)] . , φY (k, n) = φX (k, n) + φV (k, n). 53) We see how any optimal filter will try to compromise between speech intelligibility and speech quality.

One easy way is to set αy = αv . 2 plots the performance of the MVDR filter also in the white Gaussian noise, but this time with αy = αv . 0 αy Fig. 1 Fullband performance of the MVDR filter for different values of the filter length, L, as a function of the forgetting factor αy in the white Gaussian noise. 98, the window size is K = 64 (8 ms) with a 75% overlap, and the fullband input SNR is 10 dB. reduction performance. Comparing Figs. 2, we can see that varying the forgetting factor αv can help optimize the performance even when the noise is stationary.

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