A joint-optimization NSAF algorithm based on the first-order Markov   model
  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. 
Yi Yu, Haiquan Zhao
cs.SY