# Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

arXiv: https://arxiv.org/abs/2108.10219 · HTML: https://mlsanity.com/paper/2108.10219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 82 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques](https://mlsanity.com/paper/1606.01313/) (2016)
- [Direction Finding Algorithms with Joint Iterative Subspace Optimization](https://mlsanity.com/paper/1402.2710/) (2014)
- [Sparsity-Aware Adaptive Algorithms Based on Alternating Optimization with Shrinkage](https://mlsanity.com/paper/1401.0463/) (2014)
- [Generalized Correntropy for Robust Adaptive Filtering](https://mlsanity.com/paper/1504.02931/) (2015)
- [Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering](https://mlsanity.com/paper/1608.00441/) (2016)

## Abstract (arXiv)

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.