Subband blind source separation considering acoustic reverberation characteristics

Subband blind source separation methods have been recently proposed with the objective of reducing the computational complexity and improving the convergence rate of the online algorithms. Oversampled subband structures with DFT filter banks are usually employed in order to avoid aliasing effects and keep enough samples to estimate the statistics of the subband signals. In this paper we present a critically sampled subband structure, composed of cosine-modulated filter banks and reduced-order adaptive subfilters, for convolutive blind source separation. Its performance is compared to those of the fullband algorithm, of an oversampled subband algorithm and of a frequency-domain algorithm. We also evaluate, through computer simulations, the impact of reducing the order of the high-frequency subfilters on the separation results for different reverberation characteristics.