• Corpus ID: 249191234

HEAR: Holistic Evaluation of Audio Representations

@inproceedings{Turian2022HEARHE,
  title={HEAR: Holistic Evaluation of Audio Representations},
  author={Joseph P. Turian and Jordie Shier and Humair Raj Khan and Bhiksha Raj and Bj{\"o}rn Schuller and C. Steinmetz and Colin Malloy and George Tzanetakis and Gissel Velarde and Kirk McNally and Max Henry and Nicolas Pinto and Camille Noufi and Christian Clough and Dorien Herremans and Eduardo Fonseca and Jesse Engel and Justin Salamon and Philippe Esling and Pranay Manocha and Shinji Watanabe and Zeyu Jin and Yonatan Bisk},
  year={2022}
}
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. HEAR was launched as a NeurIPS 2021 shared challenge… 

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