Application of Blind Source Separation for Speech-Music Separation

In this paper, the application of the Independent Component Analysis In this paper, the application of the Independent Component Analysis technique in speech-music separation is discussed. The separation algorithm is in the time domain. It needs the score function estimation to minimize the mutual information. For estimating score function, sufficient samples of the mixed (speech-music) signals are needed. In other words, these samples must be included both original sources. Since the speech and music signals could contain the silent gaps, the frame selection is important in our problem. Our proposed method for selecting the optimum frame is based on the score function difference. The experimental results show good performance of the proposed method in elimination of the silent gaps. Also they express the separation algorithm based on Gaussian Mixture estimator achieves a better separation performance and less processing time compared to the separation algorithm based on Minimum Mean Square Error estimator.