Details
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence in Music, Sound, Art and Design |
| Subtitle of host publication | 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings |
| Editors | Tiago Martins, Nereida Rodríguez-Fernández, Sérgio M. Rebelo |
| Pages | 101-116 |
| Number of pages | 16 |
| ISBN (electronic) | 978-3-031-03789-4 |
| Publication status | Published - 2022 |
Publication series
| Name | Lecture Notes in Computer Science (LNCS) |
|---|---|
| Publisher | Springer |
| Volume | 13221 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. The sound is not only determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for classification and extraction of guitar effects from audio samples containing guitar, bass, keyboard and drums. The CNN was compared to baseline methods previously proposed like support vector machines and shallow neural networks together with predesigned features. The CNN outperformed all baselines, achieving a classification accuracy of up to 97.4% and a mean absolute parameter extraction error of below 0.016 for the distortion, below 0.052 for the tremolo and below 0.038 for the slapback delay effect achieving or surpassing the presumed human expert error of 0.05.
Keywords
- Convolutional neural networks, Guitar effects, Music information retrieval, Parameter extraction
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Artificial Intelligence in Music, Sound, Art and Design: 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings. ed. / Tiago Martins; Nereida Rodríguez-Fernández; Sérgio M. Rebelo. 2022. p. 101-116 (Lecture Notes in Computer Science (LNCS); Vol. 13221).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks.
AU - Hinrichs, Reemt
AU - Gerkens, Kevin
AU - Ostermann, Jörn
PY - 2022
Y1 - 2022
N2 - Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. The sound is not only determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for classification and extraction of guitar effects from audio samples containing guitar, bass, keyboard and drums. The CNN was compared to baseline methods previously proposed like support vector machines and shallow neural networks together with predesigned features. The CNN outperformed all baselines, achieving a classification accuracy of up to 97.4% and a mean absolute parameter extraction error of below 0.016 for the distortion, below 0.052 for the tremolo and below 0.038 for the slapback delay effect achieving or surpassing the presumed human expert error of 0.05.
AB - Guitar effects are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. The sound is not only determined by the choice of the guitar effect, but also heavily depends on the parameter settings of the effect. Previous research focused on the classification of guitar effects and extraction of their parameter settings from solo guitar audio recordings. However, more realistic is the classification and extraction from instrument mixes. This work investigates the use of convolution neural networks (CNNs) for classification and extraction of guitar effects from audio samples containing guitar, bass, keyboard and drums. The CNN was compared to baseline methods previously proposed like support vector machines and shallow neural networks together with predesigned features. The CNN outperformed all baselines, achieving a classification accuracy of up to 97.4% and a mean absolute parameter extraction error of below 0.016 for the distortion, below 0.052 for the tremolo and below 0.038 for the slapback delay effect achieving or surpassing the presumed human expert error of 0.05.
KW - Convolutional neural networks
KW - Guitar effects
KW - Music information retrieval
KW - Parameter extraction
UR - http://www.scopus.com/inward/record.url?scp=85128902005&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-03789-4_7
DO - 10.1007/978-3-031-03789-4_7
M3 - Conference contribution
SN - 978-3-031-03788-7
T3 - Lecture Notes in Computer Science (LNCS)
SP - 101
EP - 116
BT - Artificial Intelligence in Music, Sound, Art and Design
A2 - Martins, Tiago
A2 - Rodríguez-Fernández, Nereida
A2 - Rebelo, Sérgio M.
ER -