Yuan, R., Ma, Y., Li, Y. et al. (22 more authors) (2023) MARBLE: Music Audio Representation Benchmark for Universal Evaluation. In: Advances in Neural Information Processing Systems (NeurIPS 2023). 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 10-16 Dec 2023, New Orleans, USA. Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Abstract
In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark. To address this issue, we introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We establish a unified protocol based on 18 tasks on 12 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines. MARBLE offers an easy-to-use, extendable, and reproducible suite for the community, with clear statements on dataset copyright. Results suggest that recently proposed large-scale pre-trained musical language models perform the best in most tasks, with room for further improvement. The leaderboard and toolkit repository are published34 to promote future music AI research.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Jun 2024 13:22 |
Last Modified: | 07 Jun 2024 13:49 |
Published Version: | https://papers.nips.cc/paper_files/paper/2023/hash... |
Status: | Published |
Publisher: | Neural Information Processing Systems Foundation, Inc. (NeurIPS) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213153 |