Blumensath, T. and Davies, M. E.
Enforcing sparsity, shift-invariance and positivity in a bayesian model of polyphonic piano music
At IEEE Workshop on Statistical Signal Processing, France.
Full text not available from this repository.
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
Conference or Workshop Item
|Digital Object Identifier (DOI):
|Venue - Dates:
||IEEE Workshop on Statistical Signal Processing, France, 2005-07-01
||Signal Processing & Control Grp
||01 Jul 2010 10:54
||18 Apr 2017 04:20
|Further Information:||Google Scholar|
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