Blind separation of real world audio signals using
overdetermined mixtures
Alex Westner and V. Michael Bove, Jr.
MIT Media Laboratory
Proc. ICA '99, January 1999
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 filter architecture. We
use ideal inverses of acquired room impulse responses to compare the
effectiveness of different-sized separating filter configurations of
various filter 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 filters.