Paper year
2025
Paper dossier
Multimodal Raga Classification from Vocal Performances with Disentanglement and Contrastive Loss
Detail viewSimilarity handoff
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Citations
0
Authors
2
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
Not available yet
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
3
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 39
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=0.8455, citation_velocity=0.0000, topic_growth=0.8636, 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: 0.1691)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2591)
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
In top 50 at rank 23
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.8636, 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: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.5613)
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
In top 50 at rank 15
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=0.0000, topic_growth=0.8636, 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: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6045)
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: 0.0000)
Abstract
The art music of North India is rich in the use of hand gestures that accompany vocal performance. However, such gestures are idiosyncratic and are neither taught nor rehearsed by the singer. The recent availability of computer vision techniques allows us to use computational methods to analyze the accompanying gestures and look for complementarity with the audio. Using an available dataset of Hindustani raga performances by 11 singers, we extract features from audio and video (gesture) and apply deep learning models to classify the raga from short excerpts. With the gesture-based classification approximately at chance, we attempt to disentangle the singer information from the raga classification embeddings by using a gradient reversal approach. We next investigate a framework that considers the body of existing multimodal fusion techniques via experiments for the multimodal raga classification. Despite the much weaker performance of the video modality relative to audio, we achieve a singer-feature-disentangled multimodal fusion system that slightly, but consistently, outperforms the audio-only classification.
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 ProcessingAnimal Vocal Communication and BehaviorMusic Technology and Sound Studies
Neighbor surface
Similar papers
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