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Low bitrate object coding of musical audio using bayesian harmonic models
Abstract : This article deals with the decomposition of music signals into pitched sound objects made of harmonic sinusoidal partials for very low bitrate coding purposes. After a brief review of existing methods, we recast this problem in the Bayesian framework. We propose a family of probabilistic signal models combining learnt object priors and various perceptually motivated distortion measures. We design efficient algorithms to infer object parameters and build a coder based on the interpolation of frequency and amplitude parameters. Listening tests suggest that the loudness-based distortion measure outperforms other distortion measures and that our coder results in a better sound quality than baseline transform and parametric coders at 8 kbit/s and 2 kbit/s. This work constitutes a new step towards a fully object-based coding system, which would represent audio signals as collections of meaningful note-like sound objects.
Cited literature [26 references]
https://hal.inria.fr/inria-00544265
Contributor : Emmanuel Vincent <>
Submitted on : Tuesday, December 7, 2010 - 3:41:05 PM
Last modification on : Thursday, March 21, 2019 - 2:20:03 PM
Document(s) archivé(s) le : Tuesday, March 8, 2011 - 4:40:00 AM
Contributor : Emmanuel Vincent <>
Submitted on : Tuesday, December 7, 2010 - 3:41:05 PM
Last modification on : Thursday, March 21, 2019 - 2:20:03 PM
Document(s) archivé(s) le : Tuesday, March 8, 2011 - 4:40:00 AM
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vincent_TASLP07.pdf
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