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.

翻译：在艺术与人工智能（AI）广泛交叉的时代（如图像生成与小说协同创作），AI在音乐领域的应用仍处于相对初期阶段，尤其在音乐理解方面。具体表现为：深度音乐表征研究有限、大规模数据集匮乏，且缺乏通用且社区驱动的基准。为解决这一问题，我们提出面向通用评估的音乐音频表征基准——MARBLE。该基准通过定义一个包含声学、演奏、乐谱及高层描述四个层级的综合分类体系，旨在为多种音乐信息检索（MIR）任务提供基准。我们基于8个公开数据集上的14项任务建立统一协议，对所有开源预训练模型（基于音乐录音开发）的表征进行公平且标准化的评估，并将其作为基线模型。此外，MARBLE为社区提供了一套易用、可扩展且可复现的工具包，并就数据集版权问题作出明确声明。结果表明，近期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，但仍存在进一步改进空间。排行榜和工具包存储库已发布于https://marble-bm.shef.ac.uk，以推动未来音乐AI研究。