MARBLE: Music Audio Representation Benchmark for Universal Evaluation
| Autor: | Yuan, Ruibin, Ma, Yinghao, Li, Yizhi, Zhang, Ge, Chen, Xingran, Yin, Hanzhi, Zhuo, Le, Liu, Yiqi, Huang, Jiawen, Tian, Zeyue, Deng, Binyue, Wang, Ningzhi, Lin, Chenghua, Benetos, Emmanouil, Ragni, Anton, Gyenge, Norbert, Dannenberg, Roger, Chen, Wenhu, Xia, Gus, Xue, Wei, Liu, Si, Wang, Shi, Liu, Ruibo, Guo, Yike, Fu, Jie |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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 then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines. Besides, MARBLE offers an easy-to-use, extendable, and reproducible suite for the community, with a clear statement on copyright issues on datasets. Results suggest 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 at https://marble-bm.shef.ac.uk to promote future music AI research. Comment: camera-ready version for NeurIPS 2023 |
| Databáze: | arXiv |
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