Enforcing sparsity, shift-invariance and positivity in a bayesian model of polyphonic piano music
Blumensath, T. and Davies, M. E. (2005) Enforcing sparsity, shift-invariance and positivity in a bayesian model of polyphonic piano music. In, IEEE Workshop on Statistical Signal Processing, Bordeaux, FR, Institute of Electrical and Electronics Engineers . (doi:10.1109/SSP.2005.1628695).
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In this paper we develop a Bayesian method to extract individual
notes from a polyphonic piano recording. The distribution
of the note activation is non-negative and we therefore introduce
a modified Rayleigh distribution to model this note behaviour.
Sparseness of the note activation is achieved by a mixture
distribution that is a mixture of a delta function and the modified
Rayleigh distribution. The used learning rule requires integration
over the note activations, which is done using a Gibbs Sampling
Monte Carlo method. We analyse the behaviour of the algorithm
using a simplified test signal as well as a real piano recording
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Q Science > QA Mathematics
Q Science > QC Physics
|Divisions:||University Structure - Pre August 2011 > School of Mathematics
Faculty of Engineering and the Environment > Institute of Sound and Vibration Research > Signal Processing & Control Research Group
|Date Deposited:||01 Jul 2010 10:54|
|Last Modified:||06 Aug 2015 03:01|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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