Bayesian non-negative matrix factorization with learned temporal smoothness priors

Authors:
Mathieu Coïc;Juan José Burred
Affiliations:
Audionamix, Paris, France;Audionamix, Paris, France
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

Citing 3
Cited 0

Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis

Neural Computation
Enforcing harmonicity and smoothness in Bayesian non-negative matrix factorization applied to polyphonic music transcription

IEEE Transactions on Audio, Speech, and Language Processing
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation

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Abstract

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.