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Enforcing sparsity, shift-invariance and positivity in a bayesian model of polyphonic piano music

Record type: Conference or Workshop Item (Paper)

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

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Citation

Blumensath, T. and Davies, M. E. (2005) Enforcing sparsity, shift-invariance and positivity in a bayesian model of polyphonic piano music At IEEE Workshop on Statistical Signal Processing, France. (doi:10.1109/SSP.2005.1628695).

More information

Published date: July 2005
Venue - Dates: IEEE Workshop on Statistical Signal Processing, France, 2005-07-01
Organisations: Signal Processing & Control Grp

Identifiers

Local EPrints ID: 151933
URI: http://eprints.soton.ac.uk/id/eprint/151933
PURE UUID: 267ec96a-a5f5-4746-abd2-e4ea59aa0afe

Catalogue record

Date deposited: 01 Jul 2010 10:54
Last modified: 18 Jul 2017 12:55

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