New PDF release: A Conceptual Framework for Noise Reduction

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 a minimum of 5 many years, advances in our figuring out and the improvement of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted suggestions for in actual fact outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the various diverse facets of noise relief, delivering a uniform process
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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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. 68) ρTxx Φ−1 y ρxx 1 + oSNRmax = hW . 63) greater (resp. smaller) than the input SNR will result in a filter that will favor partial intelligibility (resp. quality) over quality (resp. partial intelligibility) as compared to the Wiener filter. 6 Simulations In this section, we illustrate the performance of the optimal filters derived above through simulations.

3) is the complex convolved speech signal, and vm (t) = vr,m (t) + jvr,M +m (t), m = 1, 2, . . 4) is the complex additive noise. Now, our problem may be stated as follows: given the M complex microphone signals, ym (t), m = 1, 2, . . , our desired signal) the best way we can, including the phase, which is important for the localization of the source signal. 2) can be put into a vector form if we accumulate L successive time samples: ym (t) = xm (t) + vm (t), m = 1, 2, . . 6) is a vector of length L, and xm (t) and vm (t) are defined in a similar way to ym (t).

In this case, this index measures the quality improvement. So, the smaller is this index, the larger is the quality improvement. To summarize, 30 3 Single-Channel Noise Reduction in the Time Domain if the input SNR is high, the speech quality index gets closer to 1, since the improvement can be very small in this case. Consequently, the speech quality index makes sense only when combined with the input SNR. 5 plots the performance of the Wiener and MVDR filters in the NYSE noise. Comparing Figs.

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

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