@inproceedings{a49ff0db57b44acdbe984e3c6b13a9ce,
title = "Adaptive sparsity tradeoff for ℓ1-constraint NLMS algorithm",
abstract = "Embedding the norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Even though the foundations are well understood, the selection of the sparsity hyper-parameter still remains today matter of study. Supported on the modal analysis of the adaptive algorithm near steady state, this paper shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.",
keywords = "expectation-maximization, Gaussian mixture models, modal analysis, NLMS, norm, Sparsity",
author = "Abdullah Al-Shabili and Luis Weruaga and Shihab Jimaa",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",
year = "2016",
month = may,
day = "18",
doi = "10.1109/ICASSP.2016.7472570",
language = "British English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4707--4711",
booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings",
address = "United States",
}