Submissions to the HEAR benchmark leaderboard are welcome at any time. To evaluate on the HEAR benchmark and submit your results to the leaderboard:

1. Develop an audio embedding model that follows the HEAR API.
   • You may train your embedding model on any data that you want, except for data from splits in HEAR tasks that are explicitly denoted as test splits in train/validation/test dataset split setups.
   • See hear-baseline for examples of how to wrap an existing embedding model in the HEAR API.
2. Download the HEAR benchmark tasks at the correct sample rate for your model.
   • All tasks are available for download at the following sample rates: 16000, 22050, 32000, 44100, and 48000.
   • If your model uses a sample rate that is not listed you will need to either resample the datasets or use hear-preprocess to recreate the datasets at the desired sample rate.
3. Use hear-eval-kit to:
   • a) Compute embeddings for all HEAR tasks using your model
   • b) Evaluate embeddings by training a shallow downstream classifier
4. Submit your results to the leaderboard by appending them to the CSV file located in the GitHub repository for this website and then create a pull request with the updated CSV file. Detailed submission instructions are provided in the README for the GitHub repository.