Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation
arXiv:2108.10219
Abstract
In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts.
arXiv comment12 figures, 13 pages
References in corpus (7)
- Generalized Correntropy for Robust Adaptive Filtering
- Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
- Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
- Regularized Least-Mean-Square Algorithms
- Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage
- Adaptive Reduced-Rank Minimum Symbol-Error-Rate Receive Processing for Large-Scale Multiple-Antenna Systems
- Direction Finding Algorithms with Joint Iterative Subspace Optimization