Paper year
2025
Paper dossier
Advancing guitar emotion recognition through audio data augmentation to enhance smart musical instruments
Detail viewSimilarity handoff
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Citations
1
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
Ranking details could not be loaded (API 503).
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.
Authors
No authors available.
Neighborhood labels
Topics
0 labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Neighbor surface
Similar papers
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
Next handoff
Best next moves from here
01
Check recommendation families
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Inspect nearby topics
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Cross-check evaluation baselines
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.