回声消除背景下的稀疏感知学习:一种集合理论估计方法

Y. Kopsinis, S. Chouvardas, S. Theodoridis
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引用次数: 3

摘要

本文研究了输入信号为非平稳且可能取较小值的情况下,基于集合论的自适应滤波任务。在实际应用中经常遇到这种情况,其中回波抵消技术的应用最为突出。事实证明,很小的输入值会触发算法的不良行为,导致严重的性能波动。从几何角度研究了这种故障的根源,并提出了一种符合集合论思想的解决方案。新算法在真实的回波消除场景中进行了评估,并与最先进的回波消除方法(如IPNLMS和IPAPA算法)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sparsity-aware learning in the context of echo cancelation: A set theoretic estimation approach
In this paper, the set-theoretic based adaptive filtering task is studied for the case where the input signal is nonstationary and may assume relatively small values. Such a scenario is often faced in practice, with a notable application that of echo cancellation. It turns out that very small input values can trigger undesirable behaviour of the algorithm leading to severe performance fluctuations. The source of this malfunction is geometrically investigated and a solution complying with the set-theoretic philosophy is proposed. The new algorithm is evaluated in realistic echo-cancellation scenarios and compared with state-of-the-art methods for echo cancellation such as the IPNLMS and IPAPA algorithms.
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