Nonnegative matrix factorization (NMF) has been traditionally considered a promising approach for audio source separation. While standard NMF is only suited for single-channel mixtures, extensions to consider multi-channel data have been also proposed. Among the most popular alternatives, multichannel NMF (MNMF) and further derivations based on constrained spatial covariance models have been successfully employed to separate multi-microphone convolutive mixtures. This letter proposes a MNMF extension by considering a mixture model with Ray-Space-transformed signals, where magnitude data successfully encodes source locations as frequency-independent linear patterns. We show that the MNMF algorithm can be seamlessly adapted to consider Ray-Space-transformed data, providing competitive results with recent state-of-the-art MNMF algorithms in a number of configurations using real recordings.

中文翻译：

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基于射线空间的多通道非负矩阵分解用于音频源分离

传统上，非负矩阵分解（NMF）被认为是用于音频源分离的有前途的方法。虽然标准NMF仅适用于单通道混合，但也提出了扩展以考虑多通道数据。在最流行的替代方案中，多通道NMF（MNMF）和基于受约束的空间协方差模型的进一步推导已成功用于分离多麦克风回旋混合物。这封信提出了通过考虑具有Ray-Space变换信号的混合模型来提出MNMF扩展，其中幅度数据成功地将源位置编码为与频率无关的线性模式。我们证明了MNMF算法可以无缝地考虑射线空间转换的数据，