%0 Journal Article
	%A Bruna Luisa Ramos Prado Vasques and  Mariane Rembold Petraglia and  Antonio Petraglia
	%D 2017
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 132, 2017
	%T Adaptive Filtering in Subbands for Supervised Source Separation
	%U https://publications.waset.org/pdf/10008312
	%V 132
	%X This paper investigates MIMO (Multiple-Input
Multiple-Output) adaptive filtering techniques for the application
of supervised source separation in the context of convolutive
mixtures. From the observation that there is correlation among the
signals of the different mixtures, an improvement in the NSAF
(Normalized Subband Adaptive Filter) algorithm is proposed in
order to accelerate its convergence rate. Simulation results with
mixtures of speech signals in reverberant environments show the
superior performance of the proposed algorithm with respect to the
performances of the NLMS (Normalized Least-Mean-Square) and
conventional NSAF, considering both the convergence speed and
SIR (Signal-to-Interference Ratio) after convergence.
	%P 1294 - 1298