Multichannel Adaptive Filtering with Sparseness Constraints
Multichannel Adaptive Filtering with Sparseness Constraints
复制标题
具有稀疏约束的多通道自适应滤波
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
S. Spors
中科院分区：
文献类型：
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作者：
Karim Helwani;H. Buchner;S. Spors
自适应滤波的性能可以通过结合先验系统知识来增强。在这篇文章中，我们系统地考虑了利用稀疏性的正则化策略来识别多声道系统的声室脉冲响应。由于多通道情况下的附加维度，结构化正则化似乎是自然而然的选择。基于这一概念，我们提出了一种通用的正则化Newton型算法。这一通用公式允许我们讨论多通道情况下的各种特性，并为未来高效算法的发展奠定了有价值的基础。
The performance of adaptive filtering can be enhanced by incorporating prior system knowledge. In this paper, we systematically consider regularization strategies exploiting sparseness for the identification of acoustic room impulse responses specifically for multichannel systems. Due to the additional dimensions in the multichannel case, a structured regularization appears to be a natural choice. Based on this concept, we present a generic regularized Newtontype algorithm. This generic formulation allows us to discuss various properties specific to the multichannel case and forms a valuable basis for the future development of efficient algorithms.