  Recently, the normalized subband adaptive filter (NSAF) algorithm has
attracted much attention for handling the colored input signals. Based on the
first-order Markov model of the optimal tap-weight vector, this paper provides
a convergence analysis of the standard NSAF. Following the analysis, both the
step size and the regularization parameter in the NSAF are jointly optimized in
such a way that minimizes the mean square deviation. The resulting
joint-optimization step size and regularization parameter (JOSR-NSAF) algorithm
achieves a good tradeoff between fast convergence rate and low steady-state
error. Simulation results in the context of acoustic echo cancellation
demonstrate good features of the proposed algorithm.
