NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NEUROCOMPUTING, [VOL72, ISSUE 1-3, (2008)] DOI10.1016/j.neucom.2007.12.05

Plumbley, MD

Vincent, E

Neurocomputing

English

Queen Mary Research Online

A generative model for music transcription,

A multipitch tracking algorithm for noisy speech,

Automatic transcription of piano music, in:

Bayesian analysis of western tonal music,

Bayesian computational models for inharmonicity in musical instruments, in:

Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables, in:

Efficient variational inference for the dynamic harmonic model, in:

Fast factorization-based inference for Bayesian harmonic models, in:

Handbook of parametric and nonparametric statistical procedures, 2nd Edition,

Information retrieval, 2nd Edition,

Information theory, inference, and learning algorithms,

Low bitrate object coding of musical audio using Bayesian harmonic models,

Monte Carlo statistical methods, 2nd Edition,

Multidimensional independent component analysis, in:

Musical source separation using time-frequency source priors,

on posterior dependence (left) or frequency difference (right) and integration on a uniform grid. Top: F -measure with various grid sizes (plain:

Polyphonic music transcription using note event modeling, in:

Polyphonic pitch tracking using joint Bayesian estimation of multiple frame parameters, in:

Prediction-driven computational auditory scene analysis,

Probabilistic inference using Markov chain Monte Carlo methods,

Prosody: theory and experiment,

Realtime multiple pitch observation using sparse non-negative constraints, in:

Signal processing methods for music transcription,

Efficient Bayesian inference for harmonic models via adaptive posterior factorization

International audienceHarmonic sinusoidal models are an essential tool for music audio signal analysis. Bayesian harmonic models are particularly interesting, since they allow the joint exploitation of various priors on the model parameters. However existing inference methods often rely on specific prior distributions and remain computationally demanding for realistic data. In this article, we investigate a generic inference method 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 this factorization holds true and propose two criteria to choose these subsets adaptively. We evaluate the resulting performance experimentally for the task of multiple pitch estimation using different levels of factorization

Vincent, Emmanuel

Plumbley, Mark, D.

INRIA a CCSD electronic archive server

This technical report is deprecated. Please refer to the following article instead: http://hal.inria.fr/inria-00544176/Harmonic sinusoidal models are an essential tool for audio signal analysis. Bayesian harmonic models are particularly interesting, since they allow the joint exploitation of various priors on the model parameters. However existing inference methods often rely on specific prior distributions and remain rather slow for realistic data. In this article, we investigate a generic inference method 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 this factorization holds true and propose two criteria to choose these subsets adaptively. We evaluate the resulting performance experimentally for the task of musical score transcription using different levels of factorization

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HAL Id: inria-00142935https://hal.inria.fr/inria-00142935v2Submitted on 10 Dec 2010HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Efficient Bayesian inference for harmonic models viaadaptive posterior factorizationEmmanuel Vincent, Mark PlumbleyTo cite this version:Emmanuel Vincent, Mark Plumbley. Efficient Bayesian inference for harmonic models via adaptiveposterior factorization. [Research Report] PI 1841, 2007, pp.20. ￿inria-00142935v2￿This technical report is deprecated.Please refer to the following paper instead:http://hal.inria.fr/inria-00544176/

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Efficient Bayesian inference for harmonic models via adaptive posterior factorization

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INRIA a CCSD electronic archive server

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HAL-Rennes 1

HAL-CentraleSupelec

HAL-Rennes 1

INRIA a CCSD electronic archive server

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