Abstract
We propose a semi-blind method for separation of stereo recordings of several sources. The method begins with computation of a set of cancellation filters for potential fixed positions of the sources. These filters are computed from one-source-only intervals selected upon cross-talk detection. Each source in some of the fixed positions is canceled by the corresponding filter, by which the other sources are separated. The former source can be then separated by adaptive suppression of the separated sources. To select the appropriate cancellation filter, we use Independent Component Analysis. The performance of the proposed method is verified on real-world SiSEC data with two fixed and/or moving sources.
This work was supported by Grant Agency of the Czech Republic through the project P103/11/1947.
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Málek, J., Koldovský, Z., Tichavský, P. (2012). Semi-blind Source Separation Based on ICA and Overlapped Speech Detection. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_57
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DOI: https://doi.org/10.1007/978-3-642-28551-6_57
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