Separation of Linearly Mixed Speech Signals using DWT based ICA

Abstract

Speech is the fundamental means of communication among humans. Speech production is the process of converting a linguistic message to the acoustic waveform. Separating various linearly mixed speech signals is often modelled by famous cocktail party problem and can be achieved by a technique known as Independent Component Analysis (ICA). ICA is similar to PCA and Factor analysis but it works on non-Gaussian mixture of signals. In this paper, the problem of separating linearly mixed signals is solved by using filter banks with ICA. Comparison of existing ICA technique with the one proposed is done based on experimental results which shows that the proposed algorithm over performs basic ICA.