In adaptive filtering, new structures and algorithms are frequently proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of the conventional algorithms, mainly for applications which require a large number of adaptive coefficients. In such applications, adaptive subband structures are especially atractive since the adaptation and filtering are performed at a reduced sampling rate. Recently, new adaptive subband structures were proposed, which are able of modeling exactly any finite impulse response system. In this work the performance properties of two of these structures are investigated: in the first one, composed of an analysis filter bank and sparse subfilters, there is no sampling rate alteration, while in the second one, the ouput signals of the analysis filter bank are maximally decimated. Optimization procedures are proposed to select the filter bank coefficients of such structures, which result in a reduction of the mean-square error (MSE) and/or in an improvement of the convergence behavior of the adaptation algorithms.
Adaptive signal processing; adaptive filtering; multirate processing
