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Machine Learning for Audio Synthesis
Rachel Manzelli · Brian Kulis · Sadie Allen · Sander Dieleman · Yu Zhang

Fri Jul 22 05:00 AM -- 05:00 AM (PDT) @ Room 316 - 317
Event URL: https://www.mlasworkshop.com/ »

The 1st Machine Learning for Audio Synthesis workshop at ICML will attempt to cover the space of novel methods and applications of audio generation via machine learning. These include, but are not limited to: methods of speech modeling, environmental sound generation or other forms of ambient sound, novel generative models, music generation in the form of raw audio, and text-to-speech methods. Audio synthesis plays a significant and fundamental role in many audio-based machine learning systems, including smart speakers and voice-based interaction systems, real-time voice modification systems, and music or other content generation systems.We plan to solicit original workshop papers in these areas, some of which will present contributed talks and spotlights. Alongside these presentations will be talks from invited speakers, a poster session and interactive live demo session, and an invited speaker panel.We believe that a machine learning workshop focused around generation in the audio domain would provide a good opportunity to bring together both practitioners of audio generation tools along with core machine learning researchers interested in audio, in order to hopefully forge new directions in this important area of research.

Author Information

Rachel Manzelli (Modulate)
Brian Kulis (Boston University and Amazon)
Sadie Allen (Boston University)
Sander Dieleman (DeepMind)
Yu Zhang (Google)

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