Paper year
2025
Paper dossier
Real-time playing technique recognition embedded in a smart acoustic guitar
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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=0.8415, 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: 0.1683)
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
Abstract The integration of real-time music information retrieval techniques into musical instruments is a crucial step towards smart musical instruments that can reason about the musical context. This paper presents a real-time guitar playing technique recognition system for a smart electro-acoustic guitar. The proposed system comprises a software recognition pipeline running on a Raspberry Pi 4 and is designed to listen to the guitar's audio signal and classify each note into eight playing techniques, both pitched and percussive. Real-time playing technique information is used in real-time to allow the musician to control wirelessly-connected stage equipment during performance. The recognition pipeline includes an onset detector, feature extractors, and a convolutional neural classifier. Four pipeline configurations are proposed, striking different balances between accuracy and sound-to-result latency. Results show how optimal performance improvements occur when latency constraints are increased from 15 to 45 ms, with performance varying between pitched and percussive techniques based on available audio context. Our findings highlight the challenges of generalization across players and instruments, demonstrating that accurate recognition requires substantial datasets and carefully selected cross-validation strategies. The research also reveals how individual player styles significantly impact technique recognition performance.
Authors
No authors available.
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