Audio Source Separation Based on Nonnegative Matrix Factorization with Graph Harmonic Structure
Tomohiro Ichita, Seisuke Kyochi, Keisuke Imoto · 2018
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.