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Open AccessFeature PaperArticle

A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs

by Merlijn Blaauw *,‡ and Jordi Bonada
Music Technology Group, Universitat Pompeu Fabra, 08012 Barcelona, Spain
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
This paper is an extended version of our paper published in Blaauw, M.; Bonada, J. A neural parametric singing synthesizer. In Proceedings of the 18th Annual Conference of the International Speech Communication Association (Interspeech), Stockholm, Sweden, 20–24 August 2017.
Academic Editor: Vesa Valimaki
Appl. Sci. 2017, 7(12), 1313; https://doi.org/10.3390/app7121313
Received: 3 November 2017 / Revised: 30 November 2017 / Accepted: 12 December 2017 / Published: 18 December 2017
(This article belongs to the Special Issue Sound and Music Computing)
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. View Full-Text
Keywords: singing synthesis; machine learning; deep learning; conditional generative models; autoregressive models singing synthesis; machine learning; deep learning; conditional generative models; autoregressive models
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Blaauw, M.; Bonada, J. A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs. Appl. Sci. 2017, 7, 1313.

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