Abstract
Real-time blind source separation (BSS) is a technique to recover independent sources from the mixed signals in online system without any prior knowledge of the sources and the mixing channels. This thesis is a study of BSS problem for speech signals recorded in real environment. The research can be divided into three parts. One is to decorrelate mixing signals in time-frequency domain, which use covariance matrix to measure the independence components. The ideal has to derive the algorithm that we can get the clean speech from different channel. The other is to use that characteristic of human hearing. By this way, we can change the algorithm by adding more parameter that can make the algorithm speed the computing time via low complexity, so we can went the system be implemented at real-time system. Obviously, it is not suitable in realistic environment implementation.Therefore, last is to make our system become an on-line algorithm by using speech signals are non-stationary in time domain. The processing runs accurately learning optimal values in the complicated space for these delays and attenuations with the previous and moment input. Our BSS system for acoustic source separation can implement in a realistic environment and the result show it has better performance with decreased computational complexity.