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Multichannel Noise Reduction with a Rectangular Filtering Matrix

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Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC,volume 1))

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 \(L \times NL\) 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.

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References

  1. S. Doclo, M. Moonen, GSVD-based optimal filtering for single and multimicrophone speech enhancement. IEEE Trans. Signal Process. 50, 2230–2244 (2002)

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  2. J. Benesty, J. Chen, Y. Huang, Microphone Array Signal Processing (Springer, Berlin, 2008)

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  3. S.B. Searle, Matrix Algebra Useful for Statistics (Wiley, New York, 1982)

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  4. G. Strang, Linear Algebra and Its Applications, 3rd edn. (Harcourt Brace Jovanonich, Orlando, 1988)

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Correspondence to Jacob Benesty .

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© 2011 Springer-Verlag Berlin Heidelberg

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Benesty, J., Chen, J. (2011). Multichannel Noise Reduction with a Rectangular Filtering Matrix. In: Optimal Time-Domain Noise Reduction Filters. SpringerBriefs in Electrical and Computer Engineering(), vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19601-0_5

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  • DOI: https://doi.org/10.1007/978-3-642-19601-0_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19600-3

  • Online ISBN: 978-3-642-19601-0

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