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.
For more information on HEAR please visit https://hearbenchmark.com or read our paper https://arxiv.org/abs/2203.03022
To submit to the HEAR benchmark leaderboard see instructions on our website and then submit by making a pull request here.