Fast factorization-based inference for bayesian harmonic models

Vincent, E and Plumbley, MD (2007) Fast factorization-based inference for bayesian harmonic models

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Abstract

Harmonie sinusoidal models are a fundamental tool for audio signal analysis. Bayesian harmonic models guarantee a good resynthesis quality and allow joint use of learnt parameter priors and auditory motivated distortion measures. However inference algorithms based on Monte Carlo sampling are rather slow for realistic data. In this paper, we investigate fast inference algorithms based on approximate factorization of the joint posterior into a product of independent distributions on small subsets of parameters. We discuss the conditions under which these approximations hold true and evaluate their performance experimentally. We suggest how they could be used together with Monte Carlo algorithms for a faster sampling-based inference. © 2006 IEEE.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID Vincent, E Plumbley, MD m.plumbley@surrey.ac.uk
Date :	1 December 2007
DOI :	10.1109/MLSP.2006.275533
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 13:21
Last Modified :	23 Jan 2020 18:30
URI:	http://epubs.surrey.ac.uk/id/eprint/838913

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