Adaptively robust blind audio signals separation by the minimum β-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 beta-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 outliers. Our experimental results also support the above statements.