Paper year
2025
Paper dossier
Advancing guitar emotion recognition through audio data augmentation to enhance smart musical instruments
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Citations
1
Authors
0
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0
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unknown
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Controlled edge slice
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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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
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Signals: semantic=0.8687, citation_velocity=0.0600, 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: 0.1737)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0300)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
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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=0.0000, 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: 0.0210)
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)
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Abstract
Dataset augmentation techniques have been widely used to achieve state-of-the-art results in Music Information Retrieval tasks. However, their application in music emotion recognition (MER) remains underexplored. MER methods are particularly relevant to the design of smart musical instruments (SMIs), as emotionally aware SMIs have the potential to enrich musical interaction by providing feedback to musicians or dynamically adjusting their sound properties. In this study, we analyze the effect of 11 augmentation techniques on emotion classification in guitar recordings using a convolutional neural network. Our dataset consists of approximately 400 guitar recordings labeled with four emotions: aggressiveness, relaxation, happiness, and sadness. Results indicate that time shift, time stretch, and pitch shift provide the most significant improvements in classification accuracy. Further analysis combining these techniques under different settings yielded similar performance outcomes. A listening test confirmed that the applied augmentations did not significantly alter the perceived emotional content of the recordings. These findings support the development of emotionally aware SMIs by enhancing MER accuracy through data augmentation, ultimately enabling more expressive and interactive music-making experiences.
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