Showing papers by "Diego B. Haddad published in 2011"

A New Initialization Method for Frequency-Domain Blind Source Separation Algorithms

Abstract: Frequency-domain blind source separation (BSS) techniques have been proposed with the objective of increasing the speed and/or reducing the computational complexity of conventional algorithms, mainly for applications that involve large convolutive mixture filters. In this letter, we present a new initialization method for frequency-domain BSS algorithms employing estimates of the directions of arrival and time-frequency masking, that outperforms the classical pre-whitening initialization technique. Performance and convergence time results of the proposed approach are presented using a frequency-domain BSS algorithm that exploits higher-order frequency dependencies, employing real conference room recordings and simulated data.

Subband blind source separation considering acoustic reverberation characteristics

Abstract: 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.