Comparative Study of QPSO and other methods in Blind Source Separation

Abstract

Many techniques are introduced as solutions of the Blind Source Separation mechanisms, as an Independent Component Analysis (ICA), which became most commonly used in this field. ICA methods exploit one of two properties: sample independency and/or non-Gaussianity. In our study, cocktail-party problem processed using ICA method. In this paper, we studied the performance of three technics with independent component analysis are standard FastICA, PSO, and QPSO; and compare the results of each algorithm with others according to the number of metrics (objective as SNR and SDR and subjective as signals plotting and playing). The implement of these algorithms were be made with two source signals and three source signals. As in evaluation process, the QPSO gives more accuracy results than other technics in the signals separation process. Many input speech signals of sampling frequency 8KHz, that achieve IID. also well condition, were tested for different speeches for men and/or women, also music.