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ICML
2010
IEEE

Bayesian Nonparametric Matrix Factorization for Recorded Music

9 years 9 months ago
Bayesian Nonparametric Matrix Factorization for Recorded Music
Recent research in machine learning has focused on breaking audio spectrograms into separate sources of sound using latent variable decompositions. These methods require that the number of sources be specified in advance, which is not always possible. To address this problem, we develop Gamma Process Nonnegative Matrix Factorization (GaP-NMF), a Bayesian nonparametric approach to decomposing spectrograms. The assumptions behind GaP-NMF are based on research in signal processing regarding the expected distributions of spectrogram data, and GaP-NMF automatically discovers the number of latent sources. We derive a mean-field variational inference algorithm and evaluate GaP-NMF on both synthetic data and recorded music.
Matthew D. Hoffman, David M. Blei, Perry R. Cook
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICML
Authors Matthew D. Hoffman, David M. Blei, Perry R. Cook
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