Unsupervised single-channel source separation using bayesian NMF

Onur Dikmen, Ali Taylan Cemgil

Abstract: We propose a prior structure for single-channel audio source separation using non-negative matrix factorisation. For the tonal and percussive signals, the model assigns different prior distributions to the corresponding parts of the template and excitation matrices. This partitioning enables not only more realistic modelling, but also a deterministic way to group the components into sources. This also prevents the possibility of not detecting/assigning a component and remove the need for a dataset and training. Our method only needs the number of components of each source to be set, but this does not play a crucial role in the performance. Very promising results can be obtained using the model with too few design decisions and moderate time complexity.