By Jacob Benesty, Jingdong Chen
Though noise relief and speech enhancement difficulties were studied for no less than 5 many years, advances in our knowing and the improvement of trustworthy algorithms are extra very important than ever, as they aid the layout of adapted ideas for in actual fact outlined purposes. during this paintings, the authors suggest a conceptual framework that may be utilized to the various various facets of noise relief, supplying a uniform procedure
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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Additional resources for A Conceptual Framework for Noise Reduction
The objective of the WL noise reduction ﬁlter is to make the output SNR greater than the input SNR so that the quality of the noisy signal may be enhanced. For the particular ﬁlter h = iid , where the identity ﬁlter, iid , is the ﬁrst column of the identity matrix, I2L of size 2L × 2L, we have oSNR iid = iSNR. 35) With the ﬁlter iid , the SNR cannot be improved. Now, let us introduce the quantity oSNRmax , which is deﬁned as the maximum output SNR that can be achieved through ﬁltering so that oSNR h ≤ oSNRmax , ∀h.
The global speech intelligibility index quantiﬁes the amount of the desired signal that is aﬀected by the ﬁlter. The subband and fullband global speech intelligibility indices are derived from the previous deﬁnitions: υi [h(k, n)] = (1 − ) υi [h(k, n)] + υq [h(k, n)] , k = 0, 1, . . 50) and υi [h(:, n)] = (1 − ) υi [h(:, n)] + υq [h(:, n)] . , φY (k, n) = φX (k, n) + φV (k, n). 53) We see how any optimal ﬁlter will try to compromise between speech intelligibility and speech quality. 54) H = h (k, n)y(k, n) − X(k, n).
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.
A Conceptual Framework for Noise Reduction by Jacob Benesty, Jingdong Chen