A neural parametric singing synthesizer modeling timbre and expression from natural songs
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- dc.contributor.author Blaauw, Merlijn
- dc.contributor.author Bonada, Jordi, 1973-
- dc.date.accessioned 2019-05-23T14:33:36Z
- dc.date.available 2019-05-23T14:33:36Z
- dc.date.issued 2017
- dc.description.abstract We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.
- dc.description.sponsorship This work is partially supported by the Spanish Ministry of Economy and Competitiveness under the CASAS project (TIN2015-70816-R).
- dc.format.mimetype application/pdf
- dc.identifier.citation Blaauw M, Bonada J. A neural parametric singing synthesizer modeling timbre and expression from natural songs. Appl Sci. 2017;7(1313): 23 p. DOI: 10.3390/app7121313
- dc.identifier.doi http://dx.doi.org/10.3390/app7121313
- dc.identifier.issn 2076-3417
- dc.identifier.uri http://hdl.handle.net/10230/37284
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.ispartof Applied Sciences. 2017;7(1313): 23 p.
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-70816-R
- dc.rights © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Singing synthesis
- dc.subject.keyword Machine learning
- dc.subject.keyword Deep learning
- dc.subject.keyword Conditional generative models
- dc.subject.keyword Autoregressive models
- dc.title A neural parametric singing synthesizer modeling timbre and expression from natural songs
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion