A Conceptual Framework for Noise Reduction - download pdf or read online

By Jacob Benesty, Jingdong Chen

ISBN-10: 3319129546

ISBN-13: 9783319129549

ISBN-10: 3319129554

ISBN-13: 9783319129556

Though noise aid and speech enhancement difficulties were studied for no less than 5 many years, advances in our figuring out and the improvement of trustworthy algorithms are extra vital than ever, as they help the layout of adapted strategies for basically outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the various varied elements of noise relief, providing a uniform procedure
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. furthermore, the derivation of optimum filters is simplified, as are the functionality measures used for his or her evaluation.

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Although noise aid and speech enhancement difficulties were studied for no less than 5 a long time, advances in our realizing and the advance of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted suggestions for truly outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the various diversified points of noise relief, supplying a uniform method of monaural and binaural noise relief difficulties, within the time area and within the frequency area, and concerning a unmarried or a number of microphones.

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Extra info for A Conceptual Framework for Noise Reduction

Example text

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. 0 αy (= αv ) Fig. 3 Fullband performance of the MVDR filters for different values of the filter length, L, as a function of the forgetting factor αy (= αv ) in the NYSE noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. 2, one can see that the performance trend of the MVDR filter in the NYSE noise is similar to that in the white Gaussian noise though the partial speech intelligibility index, the speech quality index, and the output SNR differ slightly in values in the two different noise cases with the same input SNR.

25) is the correlation matrix of a(k, n) ∈ {x(k, n), xi (k, n), v(k, n)}. In the rest, it is assumed that the rank of Φv (k, n) is equal to L so that its inverse exists. 3 Performance Measures In this section, the performance measures, which are tailored for noise reduction in the STFT domain with interframe correlation, are defined. We need to distinguish between subband and fullband measures. 3 Performance Measures 35 We define the subband and fullband input SNRs at time frame n as [1] iSNR(k, n) = iSNR(n) = φX (k, n) , k = 0, 1, .

0 αy (= αv ) Fig. 3 Fullband performance of the MVDR filters for different values of the filter length, L, as a function of the forgetting factor αy (= αv ) in the NYSE noise. The window size is K = 64 (8 ms) with a 75% overlap and the fullband input SNR is 10 dB. 2, one can see that the performance trend of the MVDR filter in the NYSE noise is similar to that in the white Gaussian noise though the partial speech intelligibility index, the speech quality index, and the output SNR differ slightly in values in the two different noise cases with the same input SNR.

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


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