Comparison of Fast ICA and Gradient Algorithms of Independent Component Analysis for Separation of Speech Signals

Abstract — Voice plays a vital role in distant communications like video conferencing, teleconferencing and hands free mobile conversion etc. Here, the quality of speech is degraded by the Cocktail party problem. Cocktail party problem is described as combination of various sources of speech signal received by a microphone. Solution for the above problem can be obtained by using Independent component Analysis (ICA), which has the ability to separate multiple speech signals into individual ones. This paper deals with application of principle of negentropy from maximization of non-gaussianity technique of ICA using Gradient and Fast ICA algorithm. The results in Matlab show that Fast ICA provides better execution time compared with gradient with minimum number of iteration. Keyword ICA, Negentropy, Fast ICA, Gradient, Maximization of non-gaussianity.