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Titlebook: Optimal Time-Domain Noise Reduction Filters; A Theoretical Study Jacob Benesty,Jingdong Chen Book 2011 Jacob Benesty 2011 LCMV filter.MDVR

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書目名稱Optimal Time-Domain Noise Reduction Filters
副標(biāo)題A Theoretical Study
編輯Jacob Benesty,Jingdong Chen
視頻videohttp://file.papertrans.cn/703/702937/702937.mp4
概述Proposes a general framework in the time domain for the single and multiple microphone cases.All known algorithms can be deduced from this new approach.Includes supplementary material:
叢書名稱SpringerBriefs in Electrical and Computer Engineering
圖書封面Titlebook: Optimal Time-Domain Noise Reduction Filters; A Theoretical Study Jacob Benesty,Jingdong Chen Book 2011 Jacob Benesty 2011 LCMV filter.MDVR
描述Additive noise is ubiquitous in acoustics environments and can affect the intelligibility and quality of speech signals. Therefore, a so-called noise reduction algorithm is required to mitigate the effect of the noise that is picked up by the microphones. This work proposes a general framework in the time domain for the single and multiple microphone cases, from which it is very convenient to derive, study, and analyze all kind of optimal noise reduction filters. Not only that all known algorithms can be deduced from this approach, shedding more light on how they function, but new ones can be discovered as well.
出版日期Book 2011
關(guān)鍵詞LCMV filter; MDVR filter; Wiener filter; maximun SNR filter; microphone arrays; multichannel; noise reduct
版次1
doihttps://doi.org/10.1007/978-3-642-19601-0
isbn_softcover978-3-642-19600-3
isbn_ebook978-3-642-19601-0Series ISSN 2191-8112 Series E-ISSN 2191-8120
issn_series 2191-8112
copyrightJacob Benesty 2011
The information of publication is updating

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https://doi.org/10.1007/978-3-642-19601-0LCMV filter; MDVR filter; Wiener filter; maximun SNR filter; microphone arrays; multichannel; noise reduct
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Multichannel Noise Reduction with a Rectangular Filtering Matrix,number of samples from each microphone signal. This time, a rectangular filtering matrix of size . is required for the estimation of the desired signal vector. The signal model is the same as in .; so we start by explaining the principle of multichannel linear filtering with a rectangular matrix.
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Single-Channel Noise Reduction with a Rectangular Filtering Matrix,e than one sample at a time. As a result, we now deal with a rectangular filtering matrix instead of a filtering vector. If . is the number of samples to be estimated and . is the length of the observation signal vector, then the size of the filtering matrix is .?×?.. Also, this approach is more gen
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