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
this https URL to promote future music AI research.

该研究提出使用音乐音频表示基准来提高音乐信息检索任务的性能，包括定义四个层次的综合分类法，并建立了 14 个任务的统一协议。研究表明，最近提出的大规模预训练音乐语言模型在大多数任务中表现最佳。