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Spatio-Temporal Regularized Recursive Least Squares Algorithm

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Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations

Part of the book series: T-Labs Series in Telecommunication Services ((TLABS))

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Abstract

Intuitively, any estimation process can profit enormously from prior knowledge. Incorporating prior knowledge into the adaptive filtering problem is typically done by means of regularization. This chapter gives a systematic consideration for regularization strategies exploiting sparseness for the identification of acoustic room impulse responses specifically for multichannel systems. The main findings of this chapter have been presented in [1]. The high convergence rates achieved by the algorithm derived in this chapter build the motivation for the subsequent chapters of this book.

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Notes

  1. 1.

    Only in this chapter of the monograph, the loudspeakers are indexed by the letter \(p\in \{1 \ldots P\}\).

References

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Authors and Affiliations

  1. Quality and Usability Lab Deutsche Telekom Laboratories, Technical University of Berlin, Berlin, Germany

    Karim Helwani

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  1. Karim Helwani
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Correspondence to Karim Helwani .

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Helwani, K. (2015). Spatio-Temporal Regularized Recursive Least Squares Algorithm. In: Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations. T-Labs Series in Telecommunication Services. Springer, Cham. https://doi.org/10.1007/978-3-319-08954-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-08954-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08953-9

  • Online ISBN: 978-3-319-08954-6

  • eBook Packages: EngineeringEngineering (R0)

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