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Optimal Time-Domain Noise Reduction FiltersMultichannel Noise Reduction with a Rectangular Filtering Matrix

Optimal Time-Domain Noise Reduction Filters: Multichannel Noise Reduction with a Rectangular... [In this last chapter, we are going to estimate L samples of the desired signal from NL observations, where N is the number of microphones and L is the number of samples from each microphone signal. This time, a rectangular filtering matrix of size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L \times NL$$\end{document} is required for the estimation of the desired signal vector. The signal model is the same as in Sect. 4.1; so we start by explaining the principle of multichannel linear filtering with a rectangular matrix.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Optimal Time-Domain Noise Reduction FiltersMultichannel Noise Reduction with a Rectangular Filtering Matrix

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Publisher
Springer Berlin Heidelberg
Copyright
© Jacob Benesty 2011
ISBN
978-3-642-19600-3
Pages
61 –75
DOI
10.1007/978-3-642-19601-0_5
Publisher site
See Chapter on Publisher Site

Abstract

[In this last chapter, we are going to estimate L samples of the desired signal from NL observations, where N is the number of microphones and L is the number of samples from each microphone signal. This time, a rectangular filtering matrix of size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L \times NL$$\end{document} is required for the estimation of the desired signal vector. The signal model is the same as in Sect. 4.1; so we start by explaining the principle of multichannel linear filtering with a rectangular matrix.]

Published: Apr 16, 2011

Keywords: Noise Reduction; Signal Vector; Residual Noise; Rectangular Matrix; Microphone Signal

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