Paper year
2025
Paper dossier
MusiQAl: A Dataset for Music Question-Answering through Audio-Video Fusion
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
1
Authors
4
Topic labels
3
Source readout
Source and corpus status
Venue
Transactions of the International Society for Music Information Retrieval
Source slug
tismir
Corpus placement
Core corpus
Similarity rows
6
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
1
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
In top 50 at rank 48
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8507, citation_velocity=0.0600, topic_growth=0.7178, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1701)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0300)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2153)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0600, topic_growth=0.7178, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0210)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.4665)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
Present in run, outside top 50
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.0600, topic_growth=0.7178, diversity_penalty=0.2789
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0180)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.5024)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Pool popularity penaltypenalty
Pool popularity penalty: reduces score when non-zero (contribution to score: -0.0697)
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.
Authors
Neighborhood labels
Topics
3 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
Neighbor surface
Similar papers
6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
- Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies
- Music and Audio ProcessingDiverse Musicological StudiesNatural Language Processing Techniques
- Music and Audio ProcessingDiverse Musicological StudiesMusic Technology and Sound Studies
- Music and Audio ProcessingEmotion and Mood RecognitionMusic Technology and Sound Studies
- Music and Audio ProcessingMusic Technology and Sound StudiesNeuroscience and Music Perception
- Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing
Next handoff
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01
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02
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03
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