2022/03/06 by Joseph Turian, Turian, Joseph, Jordie Shier +43 · 2 voices · 31 citations
Computer Science · Engineering · Mathematics · Neuroscience · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Hearing Loss and Rehabilitation #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #cs.AI #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2203.03022
openalex publication_date 2022/03/06 · arxiv published 2022/03/06 · arxiv updated 2022/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.