{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hear-2021-holistic-evaluation-of-audio","title":"HEAR: Holistic Evaluation of Audio Representations","arxiv_id":"2203.03022","date":"2022-03-06","proceeding":null,"authors":["Joseph Turian","Jordie Shier","Humair Raj Khan","Bhiksha Raj","Björn W. Schuller","Christian J. Steinmetz","Colin Malloy","George Tzanetakis","Gissel Velarde","Kirk McNally","Max Henry","Nicolas Pinto","Camille Noufi","Christian Clough","Dorien Herremans","Eduardo Fonseca","Jesse Engel","Justin Salamon","Philippe Esling","Pranay Manocha","Shinji Watanabe","Zeyu Jin","Yonatan Bisk"],"abstract":"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. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. 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It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.","url_abs":"https://arxiv.org/abs/2203.03022v3","url_pdf":"https://arxiv.org/pdf/2203.03022v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hear-2021-holistic-evaluation-of-audio","repo_url":"https://github.com/neuralaudio/hear-eval-kit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"hear-2021-holistic-evaluation-of-audio","repo_url":"https://github.com/fschmid56/efficientat_hear","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"hear-2021-holistic-evaluation-of-audio","repo_url":"https://github.com/jonahanton/ssl_audio","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"open-question","task_name":"Open-Ended Question Answering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2203.03022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03022"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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