A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation

R. Mazur; A. Mertins

doi:10.1109/ICASSP.2011.5946902

A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation

Institute of Signal Processing

Abstract

In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is the transformation to the time-frequency domain, where the convolution becomes a multiplication. This allows for the use of well-known instantaneous ICA algorithms independently in each frequency bin. However, this simplification leads to the problem of correctly aligning these single bins previously to the transformation to the time domain. Here, we propose a new criterion for solving this ambiguity. The new approach is based on the sparsity of the speech signals and yields a robust depermutation algorithm. The results will be shown on real world examples.

Original language	English
Title of host publication	2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages	4
Publisher	IEEE
Publication date	01.05.2011
Pages	1996-1999
ISBN (Print)	978-1-4577-0538-0
ISBN (Electronic)	978-1-4577-0539-7
DOIs	https://doi.org/10.1109/ICASSP.2011.5946902
Publication status	Published - 01.05.2011
Event	36th IEEE International Conference on Acoustics, Speech, and Signal Processing - Prague, Czech Republic Duration: 22.05.2011 → 27.05.2011 Conference number: 85875

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Mazur, R., & Mertins, A. (2011). A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1996-1999). (IEEE International Conference on Acoustics, Speech and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2011.5946902

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title = "A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation",

abstract = "In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is the transformation to the time-frequency domain, where the convolution becomes a multiplication. This allows for the use of well-known instantaneous ICA algorithms independently in each frequency bin. However, this simplification leads to the problem of correctly aligning these single bins previously to the transformation to the time domain. Here, we propose a new criterion for solving this ambiguity. The new approach is based on the sparsity of the speech signals and yields a robust depermutation algorithm. The results will be shown on real world examples.",

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Mazur, R & Mertins, A 2011, A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation. in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, pp. 1996-1999, 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, Prague, Czech Republic, 22.05.11. https://doi.org/10.1109/ICASSP.2011.5946902

A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation. / Mazur, R.; Mertins, A.
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. p. 1996-1999 (IEEE International Conference on Acoustics, Speech and Signal Processing).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AB - In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is the transformation to the time-frequency domain, where the convolution becomes a multiplication. This allows for the use of well-known instantaneous ICA algorithms independently in each frequency bin. However, this simplification leads to the problem of correctly aligning these single bins previously to the transformation to the time domain. Here, we propose a new criterion for solving this ambiguity. The new approach is based on the sparsity of the speech signals and yields a robust depermutation algorithm. The results will be shown on real world examples.

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A sparsity based criterion for solving the permutation ambiguity in convolutive blind source separation

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