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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Additional resources for A Conceptual Framework for Noise Reduction
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.