Exploiting Acoustic Similarity of Propagating Paths for Audio Signal Separation

Blind signal separation can easily find its position in audio applications where mutually independent sources need to be separated from their microphone mixtures while both room acoustics and sources are unknown. However, the conventional separation algorithms can hardly be implemented in real time due to the high computational complexity. The computational load is mainly caused by either direct or indirect estimation of thousands of acoustic parameters. Aiming at the complexity reduction, in this paper, the acoustic paths are investigated through an acoustic similarity index (ASI). Then a new mixing model is proposed. With closely spaced microphones (5–10 cm apart), the model relieves the computational load of the separation algorithm by reducing the number and length of the filters to be adjusted. To cope with real situations, a blind audio signal separation algorithm (BLASS) is developed on the proposed model. BLASS only uses the second-order statistics (SOS) and performs efficiently in frequency domain.