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

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 a long time, advances in our figuring out and the advance of trustworthy algorithms are extra vital than ever, as they help the layout of adapted suggestions for sincerely outlined functions. during this paintings, the authors suggest a conceptual framework that may be utilized to the numerous diverse points of noise aid, supplying a uniform technique
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. additionally, 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

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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. , 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.

13) Since the signals xr,1 (t) and xr,M +1 (t) come from the same source, they are in general correlated. 13) should not be true. Therefore, we can safely state that the complex desired signal, x1 (t), is noncircular, and so is the complex microphone signal, y1 (t). , v(t) is a second-order CCRV]. Since we deal with noncircular CRVs as demonstrated above, the classical linear estimation technique [7], which is developed for processing real signals or CCRVs, cannot be applied. 16) are the augmented WL filter and observation vector, respectively, both of length 2M L.

427–439, 2010. Chapter 4 Single-Channel Noise Reduction in the STFT Domain with Interframe Correlation In the previous chapter, we studied single-channel noise reduction in the time domain. In this chapter, we study the same problem but in the more convenient short-time Fourier transform (STFT) domain. Contrary to most conventional approaches, we do not assume here that successive STFT frames are uncorrelated. As a consequence, the interframe correlation is now taken into account and a filter is used in each subband instead of just a gain to enhance the noisy signal.

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