Loading...
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
Benetos, Emmanouil Chen, Wenhu Chen, Xingran Dannenberg, Roger Deng, Binyue Fu, Jie Guo, Yike Gyenge, Norbert Huang, Jiawen Le, Zhuo
Benetos, Emmanouil
Chen, Wenhu
Chen, Xingran
Dannenberg, Roger
Deng, Binyue
Fu, Jie
Guo, Yike
Gyenge, Norbert
Huang, Jiawen
Le, Zhuo
Files
Loading...
075280-1722open.pdf
Adobe PDF, 1.76 MB
Author
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
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 published to promote future music AI research.
Citation
E. Benetos, W. Chen, X. Chen, R. Dannenberg, B. Deng, J. Fu , et al., "MARBLE: Music Audio Representation Benchmark for Universal Evaluation," 2023, pp. 39626-39647.
Source
Advances in Neural Information Processing Systems
Conference
Advances in Neural Information Processing Systems 36
Keywords
Subjects
Source
Advances in Neural Information Processing Systems 36
Publisher
Curran Associates