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 knowing and the improvement of trustworthy algorithms are extra very important than ever, as they help the layout of adapted options for in actual fact outlined functions. during this paintings, the authors suggest a conceptual framework that may be utilized to the various various features of noise aid, supplying 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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Extra resources for A Conceptual Framework for Noise Reduction
OSNRmax = λmax . 37) The ﬁlter that can achieve oSNRmax is called the maximum SNR ﬁlter and is denoted by hmax . 38) where ς = 0 is an arbitrary complex number. Clearly, we have oSNRmax = oSNR hmax ≥ oSNR iid = iSNR. 40) −1 φ x1 ρ H xx1 Φin ρxx1 . We deﬁne the array gain as G h = oSNR h iSNR . 41) We easily deduce that the maximum array gain is −1 Gmax = φv1 ρH xx1 Φin ρxx1 ≥ 1. 42) 58 5 Binaural Noise Reduction in the Time Domain The partial speech intelligibility index measures the amount of the desired signal, x1 (t), that is cancelled by the WL ﬁlter.
In general, the optimal value of αy depends on both the stationarity of the signal of interest and the noise as well as the ﬁlter length, L. It should be tuned based on the application scenario for the best noise reduction performance. Another important observation we can make from Fig. 1 is that using multiple STFT frames can greatly help improve noise reduction performance. In comparison with the single-frame case where it is a unity gain, the MVDR ﬁlter using two consecutive frames can improve the SNR by more than 3 dB if the forgetting factor αy is properly chosen as seen from Fig.
K − 1, by passing Y (k, n), k = 0, 1, . . 14) l=0 H = h (k, n)y(k, n), k = 0, 1, . . , K − 1, where L is the number of consecutive time frames, the superscript conjugate-transpose operator, and h(k, n) = H0 (k, n) H1 (k, n) · · · HL−1 (k, n) H is the T is a complex-valued ﬁlter of length L. The case L = 1 corresponds to the conventional STFT-domain approach where the consecutive time frames are assumed to be uncorrelated. 18) is the residual noise. We observe that the estimate of the desired signal is the sum of three terms that are mutually uncorrelated.
A Conceptual Framework for Noise Reduction by Jacob Benesty, Jingdong Chen