Blind Separation Of Real World Audio Signals Using Overdetermined Mixtures

We discuss the advantages of using overdetermined mixtures to improve upon blind source separation algorithms that are designed to extract sound sources from acoustic mixtures. A study of the nature of room impulse responses helps us choose an adaptive lter architecture. We use ideal inverses of acquired room impulse responses to compare the eeectiveness of diierent-sized separating lter conngurations of various lter lengths. Using a multi-channel blind least-mean-square algorithm (MBLMS), we show that, by adding additional sensors, we can improve upon the separation of signals mixed with real world lters.