Paper year
2025
Paper dossier
MusiQAl: A Dataset for Music Question-Answering through Audio-Video Fusion
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1
Authors
4
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3
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Source and corpus status
Venue
Transactions of the International Society for Music Information Retrieval
Source slug
tismir
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6
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Abstract
Music question-answering (MQA) is a machine learning task where a computational system analyzes and answers questions about music‑related data. Traditional methods prioritize audio, overlooking visual and embodied aspects crucial to music performance understanding. We introduce MusiQAl, a multimodal dataset of 310 music performance videos and 11,793 human‑annotated question-answer pairs, spanning diverse musical traditions and styles. Grounded in musicology and music psychology, MusiQAl emphasizes multimodal reasoning, causal inference, and cross‑cultural understanding of performer-music interaction. We benchmark AVST and LAVISH architectures on MusiQAI, revealing strengths and limitations, underscoring the importance of integrating multimodal learning and domain expertise to advance MQA and music information retrieval.
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Topics
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Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
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Similar papers
6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3
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