Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation

• URL: http://arxiv.org/abs/2108.10219v1
• Date: Sat, 14 Aug 2021 22:20:09 GMT
• Title: Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation
• Authors: Gang Guo, Yi Yu, Rodrigo C. de Lamare, Zongsheng Zheng, Lu Lu and Qiangming Cai
• Abstract summary: We propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios. 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.
• Score: 23.889870461547105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Bregman-divergence-based Arimoto-Blahut algorithm [53.64687146666141]
  We generalize the Arimoto-Blahut algorithm to a general function defined over Bregman-divergence system. We propose a convex-optimization-free algorithm that can be applied to classical and quantum rate-distortion theory.
  arXiv  Detail & Related papers  (2024-08-10T06:16:24Z)
• First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities [91.46841922915418]
  We present a unified approach for the theoretical analysis of first-order variation methods. Our approach covers both non-linear gradient and strongly Monte Carlo problems. We provide bounds that match the oracle strongly in the case of convex method optimization problems.
  arXiv  Detail & Related papers  (2023-05-25T11:11:31Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• Adaptive Stochastic Optimisation of Nonconvex Composite Objectives [2.1700203922407493]
  We propose and analyse a family of generalised composite mirror descent algorithms. With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem. We exploit the low-dimensional structure of the decision sets for high-dimensional problems.
  arXiv  Detail & Related papers  (2022-11-21T18:31:43Z)
• Adaptive Zeroth-Order Optimisation of Nonconvex Composite Objectives [1.7640556247739623]
  We analyze algorithms for zeroth-order entropy composite objectives, focusing on dependence on dimensionality. This is achieved by exploiting low dimensional structure of the decision set using the mirror descent method with an estimation alike function. To improve the gradient, we replace the classic sampling method based on Rademacher and show that the mini-batch method copes with non-Eucli geometry.
  arXiv  Detail & Related papers  (2022-08-09T07:36:25Z)
• Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization [27.43948386608]
  We propose a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty.
  arXiv  Detail & Related papers  (2022-05-15T03:38:13Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• Adaptive filters for the moving target indicator system [10.152838128195468]
  Two approaches to improve the convergence of adaptive algorithms are presented. The proposed approach is based on an empirical signal to interference plus noise ratio (SINR) Its effectiveness is demonstrated using simulated data.
  arXiv  Detail & Related papers  (2020-12-31T04:22:55Z)
• Asymptotic study of stochastic adaptive algorithm in non-convex landscape [2.1320960069210484]
  This paper studies some assumption properties of adaptive algorithms widely used in optimization and machine learning. Among them Adagrad and Rmsprop, which are involved in most of the blackbox deep learning algorithms.
  arXiv  Detail & Related papers  (2020-12-10T12:54:45Z)
• Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
  Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning. Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms. We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
  arXiv  Detail & Related papers  (2020-07-01T18:43:32Z)
• IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method [64.15649345392822]
  We introduce a framework for designing primal methods under the decentralized optimization setting where local functions are smooth and strongly convex. Our approach consists of approximately solving a sequence of sub-problems induced by the accelerated augmented Lagrangian method. When coupled with accelerated gradient descent, our framework yields a novel primal algorithm whose convergence rate is optimal and matched by recently derived lower bounds.
  arXiv  Detail & Related papers  (2020-06-11T18:49:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.