By Jacob Benesty, Jingdong Chen
Though noise relief and speech enhancement difficulties were studied for no less than 5 a long time, advances in our figuring out and the advance of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted options for essentially outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the numerous various facets of noise aid, supplying a uniform method
to monaural and binaural noise relief difficulties, within the time area and within the frequency area, and concerning a unmarried or a number of microphones. additionally, the derivation of optimum filters is simplified, as are the functionality measures used for his or her evaluation.
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Additional info for A Conceptual Framework for Noise Reduction
In our simulations, these statistics are estimated as follows. 102) where αy ∈ (0, 1) and αv ∈ (0, 1) are the forgetting factors that control the inﬂuence of the previous data samples on the current correlation matrix estimate (the initial estimates of these two matrices are obtained from the ﬁrst 100 signal frames with a short-time average). After the estimates of the Φy (k, n) and Φv (k, n) matrices are available at time frame n, the estimate of Φx (k, n) is computed as Φy (k, n) − Φv (k, n).
0 αy (= αv ) Fig. 2 Fullband performance of the MVDR ﬁlter for diﬀerent values of the ﬁlter length, L, as a function of the forgetting factor αy (= αv ) in the white Gaussian noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. optimized separately in an iterative way, which will be left to the reader’s investigation. 3 plots the performance of the MVDR ﬁlter in the NYSE noise. The conditions of this simulation are the same as those in the previous one except that, this time, we take the NYSE noise.
Comparing Figs. 2, we can see that varying the forgetting factor αv can help optimize the performance even when the noise is stationary. 0 αy (= αv ) Fig. 2 Fullband performance of the MVDR ﬁlter for diﬀerent values of the ﬁlter length, L, as a function of the forgetting factor αy (= αv ) in the white Gaussian noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. optimized separately in an iterative way, which will be left to the reader’s investigation.