Paper year
2025
Paper dossier
Smartwatch-Based Audio-Gestural Insights in Violin Bow Stroke Analyses
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
0
Authors
5
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
2
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=0.8397, citation_velocity=0.0000, topic_growth=0.8160, 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: 0.1679)
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: 0.2448)
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 39
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=0.8160, 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: 0.5304)
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 25
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=0.8160, 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: 0.5712)
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
Following the exposition of quantitative, identifiable idiosyncrasy in violin performance - via neural network classification - we demonstrate that smartwatch-based synchronous audio-gesture logging facilitates interpretable practice feedback in violin performance. The novelty of our approach is twofold: we exploit convenient multimodal data capture using a consumer smartwatch, recording wrist-movement and audio data in parallel. Further, we prioritise the delivery of performance insights at their most interpretable, quantifying tonal and temporal performance trends. Using such accessible hardware to observe meaningful, approachable performance insights, the feasibility of our approach is maximised for use in real-world teaching and learning environments. Presented analyses draw upon a primary dataset compiled from nine violinists executing defined performance exercises. Recordings segmented via note onset detection are subject to subsequent analyses. Trends identified include a cross-participant tendency to 'rush' up-bows versus down-bows, along with lesser temporal and tonal consistency when bowing Spiccato versus Legato.
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 ProcessingMusic Technology and Sound StudiesNeuroscience and Music Perception
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 ProcessingMusic Technology and Sound StudiesNeuroscience and Music Perception
- Music and Audio ProcessingNeuroscience and Music PerceptionMusic Technology and Sound Studies
- Music Technology and Sound StudiesMusic and Audio Processing
- Music and Audio ProcessingMusic Technology and Sound StudiesNeuroscience and Music Perception
- Music and Audio ProcessingSpeech and Audio ProcessingMusic Technology and Sound Studies
- Music and Audio ProcessingMusic Technology and Sound StudiesDiverse Musicological Studies
Next handoff
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01
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02
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03
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