BLIND SOURCE SEPARATION USING WAVELETS
A. Wims Magdalene Mary, Anto Prem Kumar, Anish Abraham Chacko · 2010
This paper is the implementation of the source separation using wavelets. In this paper, the problem considered is the enhancement and separation of speech signals corrupted by environmental acoustic noise, interferences and other speakers using array of microphones containing at least two microphones.. This work presents the implementation of the blind source separation using ICA (Independent Component Analysis). ICA is a recently developed method in which the goal is to find a linear representation of non-gaussian data so that the components are statiscally independent, or independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. The ICA algorithm that uses wavelets is used to exploit the structure in the signals of interest and thus learn the source separation more efficiently. We propose a new algorithm for blind source separation(BSS), in which frequency-domain ICA and time-domain ICA are combined to achieve a superior source-separation performances.