By Jacob Benesty, Jingdong Chen

ISBN-10: 3319129546

ISBN-13: 9783319129549

ISBN-10: 3319129554

ISBN-13: 9783319129556

Though noise relief and speech enhancement difficulties were studied for no less than 5 many years, advances in our knowing and the advance of trustworthy algorithms are extra vital than ever, as they help 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 elements of noise aid, supplying 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. furthermore, 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**

**Example text**

1) where Y (k, n), X(k, n), and V (k, n) are the STFTs of y(t), x(t), and v(t), respectively, at frequency bin k ∈ {0, 1, . . , K − 1} and time frame n. In other words, these zero-mean complex random variables are the observation, desired, and noise signals, respectively, in the STFT domain. 4) φX (k, n) = E |X(k, n)| φV (k, n) = E |V (k, n)| Ó The Author(s) 2015 J. Benesty and J. 1007/978-3-319-12955-6_4 31 32 4 Single-Channel Noise Reduction in the STFT Domain are the variances of X(k, n) and V (k, n), respectively.

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.

62) so that ho,μ can be estimated from the statistics of y(t) and v(t) only. 59), it can easily be shown that the optimal ﬁlter can be reformulated as φx −1 iSNR = oSNRmax Φin ρxx 1+μ iSNR μφv Φ−1 = 1 + μG in ρxx . 63) Comparing ho,μ with hmax [eq. 34)], we see that the two ﬁlters are equivalent up to a scaling factor. , oSNR (ho,μ ) = oSNRmax , ∀μ > 0. 65) and the speech quality index: υq (ho,μ ) = −1 μ2 φv ρTxx Φ−1 in Φv Φin ρxx (1 + μGmax ) 2 . 0 Fig. 1 A speech signal from the speaker FAKS0 of the TIMIT database.

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