Abstract
ᅟIn this paper, the signed regressor normalized subband adaptive filter (SR-NSAF) algorithm is proposed. This algorithm is optimized by L1-norm minimization criteria. The SR-NSAF has a fast convergence speed and a low steady-state error similar to the conventional NSAF. In addition, the proposed algorithm has lower computational complexity than NSAF due to the signed regressor of the input signal at each subband. The theoretical mean-square performance analysis of the proposed algorithm in the stationary and nonstationary environments is studied based on the energy conservation relation and the steady-state, the transient, and the stability bounds of the SR-NSAF are predicated by the closed form expressions. The good performance of SR-NSAF is demonstrated through several simulation results in system identification, acoustic echo cancelation (AEC) and line EC (LEC) applications. The theoretical relations are also verified by presenting various experimental results.
Highlights
Fast convergence rate and low computational complexity features are important issues for high data rate applications such as speech processing, echo cancelation, network echo cancelation, and channel equalization
In the following, the energy conservation approach [27] is applied to the signed regressor normalized subband adaptive filter (SR-normalized SAF (NSAF)) and the mean-square performance analysis of the proposed algorithms are studied in the stationary and nonstationary environments
7 Simulation results We demonstrated the performance of the proposed algorithm by several computer simulations in a system identification (SI), acoustic echo cancelation (AEC) and line echo cancelation (LEC) setups
Summary
Fast convergence rate and low computational complexity features are important issues for high data rate applications such as speech processing, echo cancelation, network echo cancelation, and channel equalization. Due to the effective features of signed regressor adaptive algorithms (low computational complexity and close convergence speed to the conventional algorithm) and to increase the performance of the SR-NLMS algorithm, this paper proposes the signed regressor NSAF (SR-NSAF) algorithm. In the following, the energy conservation approach [27] is applied to the SR-NSAF and the mean-square performance analysis of the proposed algorithms are studied in the stationary and nonstationary environments. This approach does not need a white or Gaussian assumption for the input regressors. The establishment of the SR-NSAF according to the proposed cost function This algorithm utilizes the signum of the input regressors at each subband. The number of multiplications in SR-NSAF is significantly lower than other algorithms
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