Title: MusiXQA: Advancing Visual Music Understanding in Multimodal LLMs
Primary area: datasets and benchmarks
Keywords: Multimodal Large Language Models, Visual Question Answering, Benchmark Dataset, Optical Music Recognition

Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable visual reasoning abilities in natural images, text-rich documents, and graphic designs. However, their ability to interpret music sheets remains underexplored. To bridge this gap, we introduce MusiXQA, the first comprehensive dataset for evaluating and advancing MLLMs in music sheet understanding. MusiXQA features high-quality synthetic music sheets generated via MusiXTeX, with structured annotations covering note pitch and duration, chords, clefs, key/time signatures, and text, enabling diverse visual QA tasks. Through extensive evaluations, we reveal significant limitations of current state-of-the-art MLLMs in this domain. Beyond benchmarking, we developed Phi-3-MusiX, an MLLM fine-tuned on our dataset, achieving significant performance gains over GPT-based methods. The proposed dataset and model establish a foundation for future advances in MLLMs for music sheet understanding. Code, data, and model will be released upon acceptance.

TL;DR: We present MusiXQA, the first benchmark for evaluating and improving MLLMs on symbolic visual understanding of music sheets, and introduce Phi-3-MusiX, a fine-tuned MLLM that significantly outperforms existing models on this task.