Title: Towards Multimodal Understanding of Music Scores and Performance Audio
Primary area: datasets and benchmarks
Keywords: Multimodal Large Language Models; Music Understanding; Benchmarking; Retrieval-Augmented Generation; Optical Music Recognition

Abstract: Music theory, scores, and performance audio are central modalities in music research, carrying rich information about melody, harmony, rhythm, and expressive interpretation. Yet, current multimodal large language models (MLLMs) struggle to reason jointly over symbolic and acoustic inputs, particularly when dealing with high-resolution scores and fine-grained performance signals. We introduce MuseBench, the first benchmark designed to evaluate MLLMs across three key dimensions of music understanding: (1) fundamental theory knowledge, (2) score-based reasoning, and (3) performance-level interpretation. To address these challenges, we further present MuseAgent, a multimodal retrieval-augmented large language model framework. MuseAgent employs two specialized perceptual modules: measure-wise optical music recognition (M-OMR) for sheet images and automatic music transcription (AMT) for performance audio. These modules unify heterogeneous modalities into structured textual representations (e.g., ABC notation, MusicXML, JSON), which can then be directly consumed by an LLM. A database retrieval module enables both explicit retrieval (user-driven) and implicit retrieval (agent-triggered) from symbolic and audio libraries, while also serving as a storage layer for structured music. Combined with a lightweight memory bank, MuseAgent supports multi-turn, interactive orchestration of modules according to user intent. Extensive evaluations on MuseBench show that MuseAgent outperforms general-purpose MLLMs in symbolic and performance-level reasoning, demonstrating the effectiveness of combining structured multimodal representations, retrieval/storage, and agent-based orchestration.