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arXiv · 2610.31083

MQAScore: Fine-Grained Text-Music Alignment Evaluation via Large Audio Language Model

Wei-Jaw Lee·Chun-Ping Wang·Xuanjun Chen·Yi-Hsuan Yang
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Abstract

Text-to-music alignment is commonly evaluated by the global similarity between text and audio embeddings, which collapses all described attributes into a single score and obscures which ones are missing. We instead introduce MQAScore, which verifies each atomic musical concept along five clip-level dimensions (genre, instrument, mood/theme, vocal, and usage) by the probability that a large audio language model (LALM) answers affirmatively to a binary question. To assess evaluator reliability, we construct verification labels from presence/absence annotations and LLM-guided concept substitutions, and propose the Sensitivity-Invariance Ratio, which measures how selectively an evaluator responds to meaning-changing substitutions. Experiments show that concept-level scoring improves both question-answering and similarity-based evaluators, and MQAScore can be a more reliable and interpretable fine-grained complement to global similarity for evaluating text-to-music alignment.

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Wei-Jaw Lee, Chun-Ping Wang, Xuanjun Chen, Yi-Hsuan Yang. 2026-10-22. MQAScore: Fine-Grained Text-Music Alignment Evaluation via Large Audio Language Model. https://arxiv.org/abs/2610.31083

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