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Audio Source Separation Based on Nonnegative Matrix Factorization with Graph Harmonic Structure

机译：基于图谐波结构的非负矩阵分解的声源分离

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摘要

This paper proposes a novel single-channel audio source separation based on graph-regularized nonnegative matrix factorization (NMF) taking harmonic frequency structure of each instrument into account. Since the original NMF, which is regarded as unsupervised learning, cannot readily identify the corresponding basis matrix for each target source, supervised NMFs (SNMFs) using given basis matrices learned from training sources have been extensively studied. Although SNMFs usually separate a mixed source better than NMF, the performance is degraded when training sources different from the observed source. The proposed SNMF does not use learned basis matrices but uses learned graph Laplacian matrices characterizing a harmonic frequency structure of training sources for regularization. Even if training sources are different from target sources, the graph structures from observed and training sources are more correlated, thus, as experimental results show, it can separate more robustly.
机译：本文提出了一种基于图形正规化的非负面矩阵分解（NMF）的新型单通道音频源分离，考虑了每个仪器的谐波频率结构。由于被视为无监督学习的原始NMF不能容易地识别每个目标源的相应基矩阵，因此已经广泛研究了从训练源中学到的给定基矩阵的监督NMF（SNMF）已经过度研究。虽然SNMFS通常比NMF更好地分离混合源，但是当训练源与观察到的源不同的训练源进行劣化。所提出的SNMF不使用学习基矩阵，但是使用学习的图表拉普拉斯矩阵，其表征了训练来源的谐波频率结构进行正规化。即使培训源与目标来源不同，观察到的和训练源的图形结构也更相关，因此作为实验结果表明，它可以更加强大地分离。

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