HEAR 2021 NeurIPS Challenge (Holistic Evaluation of Audio Representations)

HEAR 2021 NeurIPS Challenge (Holistic Evaluation of Audio Representations)

Mon Jul-5-2021 | 01:00am - Sun Oct-31-2021 | 11:30pm

Organizer: HEAR 2021 Committee

Description

We invite you to participate in the HEAR 2021 NeurIPS Challenge (Holistic Evaluation of Audio Representations)
 
SUMMARY:
  • HEAR 2021 challenges you to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
  • We will evaluate audio representations using a benchmark suite of secret tasks across a variety of domains, including speech, environmental sound, and music.
  • You can submit an existing published model, or new work.
  • You have until October 31st 2021 to submit to the challenge.
  • PMLR will publish a HEAR special issue. All HEAR participants are invited to submit in January 2022.
 
Please see our site to learn more about the challenge here: https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html
 
We also welcome any questions you may have!
 
p.s. Excited by the HEAR competition and believe the audio ML community benefits from broad-scale evaluation? Please forward this email to relevant collaborators and mailing lists! As a new challenge, it helps us a lot if people talk about HEAR.
 
   The HEAR 2021 Committee
   Bhiksha Raj
   Björn W. Schuller
   Camille Noufi
   Christian James Steinmetz
   Colin Malloy
   Dorien Herremans
   George Tzanetakis
   Gissel Velarde
   Jesse Engel
   Jordie Shier
   Joseph Turian
   Justin Salamon
   Kirk McNally
   Max Henry
   Nicolas Pinto
   Philippe Esling
   Pranay Manocha
   Shinji Watanabe
   Yonatan Bisk
   Zeyu Jin

Register

Conference link

Details

  • Date and Time

    Start: Mon Jul-5-2021 | 01:00am

    End: Sun Oct-31-2021 | 11:30pm

  • Timezone

    (GMT-11:00) Midway Island

  • Location

    Online

  • Event Type

    Other

  • Verticals

    audio representations benchmark, speech, sound, music.,Machine Learning,Deep Learning

  • Posted by
    Gissel Velarde
    Gissel Velarde

    July 05 2021

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