New efficient subband structures for blind source separation

Abstract

Convolutive mixtures of signals generated by more than one source and acquired by a set of microphones are commonly found in acoustic signal processing applications. Subband methods have been proposed to reduce computational complexity and improve the convergence rate of adaptive algorithms developed for blind source separation of these mixtures, without significantly impairing steady-state performance. Oversampled discrete Fourier transform (DFT) filter bank is a usual option for the generation of subband signals in order to avoid harmful aliasing effects, thereby maintaining sufficient samples to estimate the subband signal statistics. In this paper, two new subband structures, composed of cosine-modulated filter banks (CMFB) with critical or oversampled sampling and low-order adaptive subfilters, are proposed for efficient blind source separation approach in convolutive mixtures of speech signals. A time-varying step-size procedure that provides better convergence rate for several reverberation characteristics is advanced. In addition, both computational complexity and steady-state performance of the proposed structures are compared to those of the standard fullband algorithm, to other subband structures with oversampling and critical sampling, and to frequency domain algorithm. The advanced solutions are capable of improving the source interference ratio (SIR) by more than 5 dB. Finally, two strategies are presented to minimize the effects of remaining aliasing in the proposed subband approaches, which obtain an additional gain of about 3 dB in the asymptotic SIR.