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Title:
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Metrical-accent aware vocal onset detection in polyphonic audio
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Author:
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Dzhambazov, Georgi Bogomilov; Holzapfel, Andre; Srinivasamurthy, Ajay; Serra, Xavier
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Abstract:
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Comunicació presentada a la ISMIR 2017: 18th International Society for Music Information Retrieval Conference, celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina. |
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Abstract:
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The goal of this study is the automatic detection of onsets of the singing voice in polyphonic audio recordings. Starting with a hypothesis that the knowledge of the current position in a metrical cycle (i.e. metrical accent) can improve the accuracy of vocal note onset detection, we propose a novel probabilistic model to jointly track beats and vocal note onsets. The proposed model extends a state of the art model for beat and meter tracking, in which a-priori probability of a note at a specific metrical accent interacts with the probability of observing a vocal note onset. We carry out an evaluation on a varied collection of multi-instrument datasets from two music traditions (English popular music and Turkish makam) with different types of metrical cycles and singing styles. Results confirm that the proposed model reasonably improves vocal note onset detection accuracy compared to a baseline model that does not take metrical position into account. |
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Abstract:
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This work is partly supported by the European Research
Council under the European Union’s Seventh Framework
Program, as part of the CompMusic project (ERC grant
agreement 267583) and partly by the Spanish Ministry
of Economy and Competitiveness, through the ”Mar´ıa de
Maeztu” Programme for Centres/Units of Excellence in
R&D” (MDM-2015-0502). |
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Rights:
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© Georgi Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy,
Xavier Serra. Licensed under a Creative Commons Attribution
4.0 International License (CC BY 4.0). Attribution: Georgi
Dzhambazov, Andre Holzapfel, Ajay Srinivasamurthy, Xavier Serra.
“Metrical-accent aware vocal onset detection in polyphonic audio”,
18th International Society for Music Information Retrieval Conference,
Suzhou, China, 2017.
https://creativecommons.org/licenses/by/4.0/
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Document type:
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Conference Object
Article - Published version |
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Published by:
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International Society for Music Information Retrieval (ISMIR)
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