Improving the performance of the instantaneous Blind audio Source Separation algorithms

Several algorithms for instantaneous blind source separation (BSS) have been introduced in the past years. The performance of these algorithms needs to be evaluated and assessed to study their merits and choose the best of them for a given application. In this paper, a new adaptive approach is presented to evaluate different blind source separation algorithms. In this new approach, three new evaluation metrics are added. The first metric is the minimum number of samples required for a successful separation process. The second metric is the time needed to complete the separation process. The third metric is the number of sources that the BSS algorithm can separate from their mixtures. The new approach is used to compare three different blind source separation algorithms. These algorithms are: kurtosis, negentropy, and the maximum likelihood. Since the evaluation of a BSS technique is application-dependent, we are using the same application (separation of audio sources) to evaluate each of these BSS algorithms. The comparison, between the three algorithms, shows that the maximum likelihood has the best performance and the kurtosis is the faster. This motivates us to develop a new hybrid approach that combines the two algorithms to gain the benefits from both algorithms. In this new algorithm we start with the maximum likelihood (ML) algorithm to find the separation matrix and then tune this matrix by the kurtosis algorithm.