Application of independent component analysis for speech–music separation using an efficient score function estimation

In this paper speech-music separation using Blind Source Separation is discussed. The separating algorithm is based on the mutual information minimization where the natural gradient algorithm is used for minimization. In order to do that, score function estimation from observation signals (combination of speech and music) samples is needed. The accuracy and the speed of the mentioned estimation will affect on the quality of the separated signals and the processing time of the algorithm. The score function estimation in the presented algorithm is based on Gaussian mixture based kernel density estimation method. The experimental results of the presented algorithm on the speech-music separation and comparing to the separating algorithm which is based on the Minimum Mean Square Error estimator, indicate that it can cause better performance and less processing time