Demixing Challenge Workshop

Music Demixing Challenge ISMIR 2021
#1

Hi, I just want to remind everyone again that we are calling for participants for the music demixing workshop that will take place online on November 12th. Everyone who submitted to the ai crowd challenge is very well invited to submit a poster, talk or discussion. It doesn’t need to be state-of-the-art!

The MDX21 workshop (https://mdx-workshop.github.io) is a satellite event at ISMIR 2021 and is the official academic follow-up for the 2021 music demixing challenge organized by Sony on aicrowd, to which you participated. It will feature invited talks as well as presentations and posters, on the topic of music processing.

__The abstract deadline is next Thursday October, 28th __

The submission system is open at the following link:
https://github.com/mdx-workshop/mdx-submissions21

We accept three kinds of submissions:

  1. Posters. Everyone may submit a poster, notably you that participated in the Music Demixing Challenge. After a minimal prescreening, you would have the opportunity to present your work in the online virtual space for the conference. Pick the POSTERS category, and submit a title + short abstract.

  2. Long presentations (20min+questions), during which you can present a recent research or some topic you think could be of interest to the community. Pick the LONG TALK category, and submit a title + extended abstract for your talk.

  3. Discussions (30min), during which you propose to initiate and moderate a group discussion about a particular topic after a 5min introduction. The objective is to stimulate new ideas and collaborations on music separations. Pick the DISCUSSIONS category, and submit a title + extended abstract that describes discussion topics.

Relevant topics for submissions include:

  • music demixing / source separation
  • representation learning for musical instruments and mixtures
  • generative models for music
  • applications of music demixing and filtering
  • self-supervised music processing
  • deep neural nets for very long waveforms
  • machine learning with applications on music stems and mixture signals
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