Time-Deconvolutive CNMF for Multichannel Blind Source Separation

Thadeu L. B. Dias, L. Biscainho, W. Martins
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引用次数: 1

Abstract

This paper tackles multichannel separation of convolutive mixtures of audio sources by using complex-valued nonnegative matrix factorization (CNMF). We extend models proposed by previous works and show that one may tailor advanced single-channel NMF techniques, such as the deconvolutive NMF, to the multichannel factorization scheme. Additionally, we propose a regularized cost function that enables the user to control the distribution of the estimated parameters without significantly increasing the underlying computational cost. We also develop an optimization framework compatible with previous related works. Our simulations show that the proposed deconvolutive model offers advantages when compared to the simple NMF, and that the regularization is able to steer the parameters towards a solution with desirable properties. Keywords— Blind source separation, convolutive mixture, NMF, deconvolutive NMF
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多通道盲源分离的时间反卷积CNMF
本文采用复值非负矩阵分解(CNMF)方法解决了音频源卷积混合的多通道分离问题。我们扩展了先前工作提出的模型,并表明可以将先进的单通道NMF技术,如反卷积NMF,定制为多通道分解方案。此外,我们提出了一个正则化的成本函数,使用户能够控制估计参数的分布,而不会显著增加底层的计算成本。我们还开发了一个与以往相关工作兼容的优化框架。我们的模拟表明,与简单的NMF相比,所提出的反卷积模型具有优势,并且正则化能够将参数导向具有理想特性的解决方案。关键词:盲源分离,卷积混合,NMF,反卷积NMF
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