BLIND SOURCE SEPARATION USING FREQUENCY INDEPENDENT COMPONENT ANALYSIS

The need for speech enhancement is very important, because of the acoustic environment we are living in, which is composed of noise and other atmospheric disturbances, and this makes it almost impossible to record a speech signal in pure form. In most of the mixed signals there is usually no information about each source. In such situation the estimates of the original source signals is done based on the information of the received mixed signals, therefore the approach to be adopted in such cases to separate the signals must be one that does it blindly, thus the method Blind Source Separation is used in this work. Our thesis work focuses on Frequency domain Blind Source Separation (BSS) in which the received mixed signals are converted into the frequency domain and Independent Component Analysis (ICA) is applied at each frequency bin. Our main target in this project is to solve the permutation and scaling ambiguities in real time applications using the method proposed by Minje et al in [12]. Our results show that this method works better in an "offline" mixtures than in real time and lastly we gave some suggestions to improve the results.

Gozie Okwelume, received his MSc degree in Elec. Eng. from Blekinge Inst. of Tech. Sweden in 2007. He worked as a Systems Manager at Hornitex Nigeria LTD. Kingsley Ezeude, received his MSc degree in Elec. Eng. from Blekinge Inst. of Tech. Sweden in 2007, and MSc in Information System from Concordia Uni. College of Alberta, Canada in 2010.