We combine the use of a Bayesian NMF framework to add temporal smoothness priors, with a supervised prior learning of the smoothness parameters on a database of solo musical instruments. The goal is to separate main instruments from realistic mono musical mixtures. The proposed learning step allows a better initialization of the spectral dictionaries and of the smoothness parameters. This approach is shown to outperform the separation results compared to the unsupervised version.
['Mathieu Coïc', 'Juan José Burred']
Bayesian non-negative matrix factorization with learned temporal smoothness priors