Paper year
2025
Paper dossier
Science of music-based citizen science: How seeing influences hearing
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Citations
0
Authors
0
Topic labels
0
Source readout
Source and corpus status
Venue
Unknown venue
Source slug
unknown
Corpus placement
Controlled edge slice
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
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
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=0.8805, citation_velocity=0.0000, topic_growth=0.0000, 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: 0.1761)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
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=0.0000, topic_growth=0.0000, diversity_penalty=1.0000
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: low; used in final ranking (contribution to score: 0.0000)
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: -0.2000)
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Abstract
Citizen science engages volunteers to contribute data to scientific projects, often through visual annotation tasks. Hearing based activities are rare and less well understood. Having high quality annotations of performed music structures is essential for reliable algorithmic analysis of recorded music with applications ranging from music information retrieval to music therapy. Music annotations typically begin with an aural input combined with a variety of visual representations, but the impact of the visuals and aural inputs on the annotations are not known. Here, we present a study where participants annotate music segmentation boundaries of variable strengths given only visuals (audio waveform or piano roll) or only audio or both visuals and audio simultaneously. Participants were presented with the set of 33 contrasting theme and variations extracted from a through-recorded performance of Beethoven's 32 Variations in C minor, WoO 80, under differing audiovisual conditions. Their segmentation boundaries were visualized using boundary credence profiles and compared using the unbalanced optimal transport distance, which tracks boundary weights and penalizes boundary removal, and compared to the F-measure. Compared to annotations derived from audio/visual (cross-modal) input (considered as the gold standard for our study), boundary annotations derived from visual (unimodal) input were closer than those derived from audio (unimodal) input. The presence of visuals led to larger peaks in boundary credence profiles, marking clearer global segmentations, while audio helped resolve discrepancies and capture subtle segmentation cues. We conclude that audio and visual inputs can be used as cognitive scaffolding to enhance results in large-scale citizen science annotation of music media and to support data analysis and interpretation. In summary, visuals provide cues for big structures, but complex structural nuances are better discerned by ear.
Authors
No authors available.
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