Filter bank design for an adaptive subband structure with critical sampling using a new adaptation scheme

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 that require a large number of adaptive coefficients. In such applications, adaptive subband structures are specially attractive since the adaptation and filtering are performed at a reduced sampling rate. Recently, an adaptive filtering structure with critical sampling was proposed, which is able to model exactly any finite impulse response system. In this paper a convergence analysis of an LMS type adaptation algorithm with power normalized step-sizes developed for this subband structure is presented and an optimization procedure to select the filter bank coefficients is proposed, which results in fast adaptation convergence rate and reduced mean-square error.