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VaPar Synth - A Variational Parametric Model for Audio Synthesis

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Citation Author(s):
Krishna Subramani, Preeti Rao, Alexandre D'Hooge
Submitted by:
Krishna Subramani
Last updated:
18 April 2020 - 2:10am
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Krishna Subramani
Paper Code:
FR1.PF.8

Abstract 

Abstract: 

With the advent of data-driven statistical modeling and abundant computing power, researchers are turning increasingly to deep learning for audio synthesis. These methods try to model audio signals directly in the time or frequency domain. In the interest of more flexible control over the generated sound, it could be more useful to work with a parametric representation of the signal which corresponds more directly to the musical attributes such as pitch, dynamics and timbre. We present VaPar Synth - a Variational Parametric Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained on a suitable parametric representation. We demonstrate our proposed model's capabilities via the reconstruction and generation of instrumental tones with flexible control over their pitch.

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Here is the link to the code for our work:
https://github.com/SubramaniKrishna/VaPar-Synth

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