Unsupervised single-channel source separation using bayesian NMF

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 onl...

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Bibliographic Details
Published in2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics pp. 93 - 96
Main Authors Dikmen, Onur, Cemgil, A. Taylan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2009
Subjects
Acoustic applications
Acoustic signal processing
Acoustical engineering
Application software
Bayesian methods
Conferences
Data analysis
Gamma Markov Chains
Gibbs Sampler
Matrix decomposition
Metropolis-Hastings
Non-negative Matrix Factorisation
Single-Channel Source Separation
Source separation
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ISBN1424436788
9781424436781
ISSN1931-1168
DOI10.1109/ASPAA.2009.5346508

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Summary: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.
ISBN:1424436788
9781424436781
ISSN:1931-1168
DOI:10.1109/ASPAA.2009.5346508