The aim of the challenge is to push machine listening models to be as holistic as the human ear, i.e., develop a model that performs well across a variety of everyday domains. Models are meant to produce a general-purpose audio representation as a strong basis for audio classification and sequence labeling. Representations are evaluated using a benchmark suite across a variety of domains, including speech, environmental sound, and music. Results for all tasks are published at https://neuralaudio.ai/hear2021-leaderboard.html, opens an external URL in a new window.

CP's team used their latest state-of-the-art transformer model, the details of which are published in a preprint (https://arxiv.org/abs/2110.05069, opens an external URL in a new window). The source code for training such a model, and pre-trained models are available at https://github.com/kkoutini/PaSST, opens an external URL in a new window.