Independent Low-Rank Tensor Analysis for Audio Source Separation
Independent Low-Rank Tensor Analysis for Audio Source Separation
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DOI：
10.23919/eusipco.2018.8553013
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发表时间：
2018-09
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通讯作者：
Kazuyoshi Yoshii;Koichi Kitamura;Yoshiaki Bando;Eita Nakamura;Tatsuya Kawahara
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作者：
Kazuyoshi Yoshii;Koichi Kitamura;Yoshiaki Bando;Eita Nakamura;Tatsuya Kawahara
本文介绍了一种通用的张量分解技术，称为独立低秩张量分析（ILRTA）及其应用于单通道音频源分离。通常，在不现实但传统的时间-频率（TF）仓的独立性假设下，在短时傅立叶变换（STFT）域中进行音频源分离。非负矩阵分解（NMF）是一种典型的基于源谱图低秩特性的单通道源分离技术。在多通道环境中，独立分量分析（伊卡）及其多变量扩展称为独立向量分析（IVA）通常用于基于源谱图的独立性的盲源分离。独立低秩矩阵分析（ILRMA）是近年来发展起来的一种综合NMF和IVA的分析方法。为了处理TF箱的协方差，本文提出了ILRTA作为NMF的一个新的扩展。ILRMA和ILRTA的目标都是寻找独立的低秩源。一个关键的区别是，虽然ILRMA估计解混滤波器，解相关的多通道源分离的通道，ILRTA找到最佳的变换，解相关的时间帧和频率箱的STFT表示单通道源分离的方式，由NMF假设的仓独立性保持尽可能真实。我们报告ILRTA的评估结果，并讨论扩展ILRTA多通道源分离。
This paper describes a versatile tensor factorization technique called independent low-rank tensor analysis (ILRTA) and its application to single-channel audio source separation. In general, audio source separation has been conducted in the short-time Fourier transform (STFT) domain under an unrealistic but conventional assumption of the independence of time-frequency (TF) bins. Nonnegative matrix factorization (NMF) is a typical technique of single-channel source separation based on the low-rankness of source spectrograms. In a multichannel setting, independent component analysis (ICA) and its multivariate extension called independent vector analysis (IVA) have often been used for blind source separation based on the independence of source spectrograms. Integrating NMF and IVA, independent low-rank matrix analysis (ILRMA) was recently proposed. To deal with the covariance of TF bins, in this paper we propose ILRTA as a new extension of NMF. Both ILRMA and ILRTA aim to find independent and low-rank sources. A key difference is that while ILRMA estimates demixing filters that decorrelate the channels for multichannel source separation, ILRTA finds optimal transforms that decorrelate the time frames and frequency bins of a STFT representation for single-channel source separation in a way that the bin-wise independence assumed by NMF holds true as much as possible. We report evaluation results of ILRTA and discuss extension of ILRTA to multichannel source separation.