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Robust Extraction of Audio Source Signals by the Minimum ?-Divergence Method
Recently, independent component analysis (ICA) is the most popular and promising statistical technique for blind audio source separation. This paper proposes the minimum ?-divergence based ICA as an adaptive robust audio source separation algorithm. This algorithm explores local structures of audio source signals in which the observed signals follow a mixture of several ICA models. The performance of this algorithm is equivalent to the standard ICA algorithms if observed signals are not corrupted by outliers and there exist only one structure of audio source signals in the entire data space, while it keeps better performance otherwise. It is able to extract all local audio source structures sequentially in presence of huge amount of outliers. Our experimental results also agreed with the above statements.
Audio source separation, Linear and non-linear mixture data, Independent
component analysis, ICA mixture model, Minimum ?-divergence method, and Robustness
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