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：在图像生成与小说共创等艺术与人工智能（AI）广泛交叉的时代，AI 在音乐领域的应用仍相对初级，尤其在音乐理解方面。这体现在深度音乐表示研究的有限性、大规模数据集的稀缺性，以及缺乏通用且由社区驱动的基准。为解决这一问题，我们提出了面向通用评估的音乐音频表示基准，简称 MARBLE。该基准通过定义包含四个层级（声学、演奏、乐谱及高级描述）的综合分类体系，旨在为各类音乐信息检索（MIR）任务提供基准。随后，我们基于 8 个公开数据集中的 14 项任务建立了统一协议，对所有基于音乐录音开发的开源预训练模型的表示能力进行公平、标准的评估，并将其作为基线。此外，MARBLE 还提供了一套易于使用、可扩展且可复现的工具包，并对数据集版权问题进行明确说明。实验结果表明，近期提出的大规模预训练音乐语言模型在多数任务中表现最佳，但仍有进一步提升空间。排行榜与工具包仓库已发布于 https://marble-bm.shef.ac.uk，以推动未来音乐 AI 研究。