Kurtosis Maximization for Blind Speech Separation in Hindi Speech Processing System using Swarm Intelligence and ICA

Abstract

Blind source separation (BSS) method divides mixed signals blindly without any data on the mixing scheme. This is a main issue in an actual period world whether have to identify a specific person in the crowd or it is a zone of speech signal is removed. Besides, these BSS approaches are collective with shape and also statistical features to authenticate the performance of each one in outline classification. For resolving this issue proposes an active BSS algorithm on the basis of the group search optimisation (GSO). The kurtosis of the signals is used as the objective performance and the GSO is utilised to resolve it in the suggested algorithm. Primarily, source signals are taken into account as the independent component analysis (ICA) to generate the mixing signals to BSS yield the maximum kurtosis. The source signal constituent that is divided out is then smeared off from mixtures with the help of the deflation technique. Each and every source signals establish that important development of the computation amount and the quality of signal separation is attained using the projected BSS-GSO algorithm if associated with the preceding algorithms.