Abstract
We focus on convolutive mixtures, expressed in time-domain. Separation is known to be obtained by testing the independence between delayed outputs. This criterion can be much simplified and we prove in this paper that testing the independence between the contributions of all sources on the same sensor at same time index also leads to separability. We recover the contribution by using Wiener filtering (or Minimal Distorsion Principal) which is included in the separation filters. The independence is tested here with the mutual information. It is minimized only for non-delayed outputs of the Wiener filters. The test is easier and shows good results on simulation and experimental signals for the separation of piston slap and combustion in diesel engine.
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References
Yellin, D., Weinstein, E.: Criteria for multichannel signal separation. IEEE Trans. on Signal Processing, 2158–2168 (August 1994)
Papoulis, A.: Probability, random variables and stochastic processes. McGraw-Hill, New York (1991)
Babaie-Zadeh, M., Jutten, C.: Mutual information minimization: application to blind source separation. Signal Processing 85(5), 975–995 (2005)
El Rahbi, M., Gelle, G., Fenniri, H., Delaunay, G.: A penalized mutual information criterion for blind separation of convolutive mixtures. Signal Processing 84(4), 1979–1984
Matsuoka, K., Ohba, Y., Toyota, Y., Nakashima, S.: Blind separation for convolutive mixtures of many voices. In: Int. workshop on acoustic echo and noise control, Kyoto, Japan (September 2003)
Comon, P.: Contrasts for multichannel blind deconvolution. IEEE Signal Processing Letters 3(7), 209–211 (1996)
Comon, P.: Independent component analysis, a new concept. IEEE Trans. on Signal Processing 36(3), 287–314 (1994)
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© 2006 Springer-Verlag Berlin Heidelberg
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Akil, M., Servière, C. (2006). Separability of Convolutive Mixtures: Application to the Separation of Combustion Noise and Piston-Slap in Diesel Engine. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_40
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DOI: https://doi.org/10.1007/11679363_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Mutual Information
- Diesel Engine
- Discrete Fourier Transform
- Independent Component Analysis
- Blind Source Separation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
