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A Classifier-Based Approach to Score-Guided Source Separation of Musical Audio

A Classifier-Based Approach to Score-Guided Source Separation of Musical Audio Christopher Raphael University of Indiana School of Informatics 901 East 10th Street Bloomington, Indiana 47408-3912 USA craphael@indiana.edu A Classifier-Based Approach to Score-Guided Source Separation of Musical Audio Audio source separation seeks to decompose an audio recording into several different layers corresponding to independent sources, such as different speakers, or, in our case, musical parts. Source separation is a formidable task; although the problem has received considerable attention in recent years, it is safe to say that it remains open. Many approaches to this audio decomposition problem are deemed blind source separation, meaning that the audio is decomposed without explicit knowledge of its contents (Cardoso 1998; Bregman 1990; Ellis 1996). In particular, much recent work has focused on Independent Component Analysis (ICA) as the methodological backbone of various approaches (Bell and Sejnowski 1995; Lee et al. 1999). Work on blind separation also contains work specifically devoted to music audio (e.g., Maher 1990; Vincent 2006). Although blind separation is no doubt broadly useful and deeply interesting, many of the techniques rely on restrictive assumptions about the recording process or audio, often not satisfied in practice. Moreover, blind approaches seem simply wrong-headed for our purposes, because they fail to capitalize on http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computer Music Journal MIT Press

A Classifier-Based Approach to Score-Guided Source Separation of Musical Audio

Computer Music Journal , Volume 32 (1) – Mar 1, 2008

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References (22)

Publisher
MIT Press
Copyright
© 2008 Massachusetts Institute of Technology
Subject
Audio Signal Processing
ISSN
0148-9267
eISSN
1531-5169
DOI
10.1162/comj.2008.32.1.51
Publisher site
See Article on Publisher Site

Abstract

Christopher Raphael University of Indiana School of Informatics 901 East 10th Street Bloomington, Indiana 47408-3912 USA craphael@indiana.edu A Classifier-Based Approach to Score-Guided Source Separation of Musical Audio Audio source separation seeks to decompose an audio recording into several different layers corresponding to independent sources, such as different speakers, or, in our case, musical parts. Source separation is a formidable task; although the problem has received considerable attention in recent years, it is safe to say that it remains open. Many approaches to this audio decomposition problem are deemed blind source separation, meaning that the audio is decomposed without explicit knowledge of its contents (Cardoso 1998; Bregman 1990; Ellis 1996). In particular, much recent work has focused on Independent Component Analysis (ICA) as the methodological backbone of various approaches (Bell and Sejnowski 1995; Lee et al. 1999). Work on blind separation also contains work specifically devoted to music audio (e.g., Maher 1990; Vincent 2006). Although blind separation is no doubt broadly useful and deeply interesting, many of the techniques rely on restrictive assumptions about the recording process or audio, often not satisfied in practice. Moreover, blind approaches seem simply wrong-headed for our purposes, because they fail to capitalize on

Journal

Computer Music JournalMIT Press

Published: Mar 1, 2008

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