JSM 2005 Online Program

Abstract #304604

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	130
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304604
Title:	Bayesian Separation of Harmonic Sources
Author(s):	Manuel Davy*+
Companies:	LAGIS/CNRS
Address:	Ecole Centrale de Lille, Villeneuve D Ascq, 59651, France
Keywords:	Source separation ; Monte Carlo Methods ; Music Transcription
Abstract:	A standard engineering problem is that of sources separation. The standard settings require as many sensors as sources to be separated, though, in practice, we often have fewer sensors than sources. The latter problem can be addressed by modeling the sources, Here, we assume they deliver quasi-periodic (harmonic) time series. Typical such problems are rotating machines fault detection/characterization and automated music transcription. The present approach relies on modeling the sources as sums of sines and cosines with time-varying amplitudes. Inference is implemented within the Bayesian framework and selection of prior parameters distribution. A specially designed Monte Carlo algorithm is implemented and proves its soundness in terms of computation time and sources separation accuracy, as found in experiments involving rotating machines, vibration data, and stereophonic musical records.

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