By Jacob Benesty, Jingdong Chen
Though noise relief and speech enhancement difficulties were studied for a minimum of 5 a long time, advances in our realizing and the improvement of trustworthy algorithms are extra very important than ever, as they help the layout of adapted suggestions for essentially outlined functions. during this paintings, the authors suggest a conceptual framework that may be utilized to the various varied points of noise relief, supplying a uniform method
to monaural and binaural noise relief difficulties, within the time area and within the frequency area, and related to 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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Extra info for A Conceptual Framework for Noise Reduction
4 plots the results in the white Gaussian noise. 2 0 18 (c) oSNR (dB) 16 14 12 10 0 10 20 30 40 50 60 70 80 Filer length L Fig. 3 Performance of the Wiener ﬁlter as a function of the ﬁlter length, L, in the NYSE noise: (a) partial speech intelligibility index, (b) speech quality index, and (c) output SNR. The input SNR is 10 dB. speech intelligibility index, υi , of the MVDR ﬁlter is always 0 regardless of the SNR level. In comparison, this index is not zero for the Wiener ﬁlter and it decreases as the input SNR increases.
Therefore, the elements X(k, n − l), l = 0, contain both a part of the desired signal and a component that we consider as an interference. 9) 2 is the interframe correlation coeﬃcient of the signal X(k, n). 12) T E [x(k, n)X ∗ (k, n)] E |X(k, n)| 2 is the (normalized) interframe correlation vector between x(k, n) and X(k, n). 5), the signal model for noise reduction in the STFT domain can be expressed as y(k, n) = X(k, n)ρxX (k, n) + xi (k, n) + v(k, n). 13) We will see how this important expression will be used in the following sections.
Benesty and Y. Huang, A Perspective on Single-Channel Frequency-Domain Speech Enhancement. Morgan & Claypool Publishers, 2011. 3. J. Benesty and Y. Huang,“A single-channel noise reduction MVDR ﬁlter,” in Proc. IEEE ICASSP, 2011, pp. 273–276. Chapter 5 Binaural Noise Reduction in the Time Domain Binaural noise reduction is an important problem in applications where there is a need to produce two “clean” outputs from noisy observations picked up by multiple microphones. But the mitigation of the noise should be made in such a way that no audible distortion is added to the two outputs (this is the same as in the single-channel case) and meanwhile the spatial information of the desired sound source should be preserved so that, after noise reduction, the remote listener will still be able to localize the sound source thanks to his/her binaural hearing mechanism.