Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 310-316 |
| Seitenumfang | 7 |
| Fachzeitschrift | Proceedings of the International Conference on Digital Audio Effects, DAFx |
| Jahrgang | 1 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 23rd International Conference on Digital Audio Effects, eDAFx2020 - virtual, Vienna, Österreich Dauer: 9 Sept. 2020 → 11 Sept. 2020 |
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. This paper introduces a method to estimate the parameter settings of guitar effects, which makes it possible to reconstruct the effect and its settings from an audio recording of a guitar. The method utilizes audio feature extraction and shallow neural networks, which are trained on data created specifically for this task. The results show that the method is generally suited for this task with average estimation errors of ±5% − ±16% of different parameter scales and could potentially perform near the level of a human expert.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Signalverarbeitung
- Geisteswissenschaftliche Fächer (insg.)
- Musik
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in: Proceedings of the International Conference on Digital Audio Effects, DAFx, Jahrgang 1, 2020, S. 310-316.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - RECOGNIZING GUITAR EFFECTS AND THEIR PARAMETER SETTINGS
AU - Jürgens, Henrik
AU - Hinrichs, Reemt
AU - Ostermann, Jörn
PY - 2020
Y1 - 2020
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. This paper introduces a method to estimate the parameter settings of guitar effects, which makes it possible to reconstruct the effect and its settings from an audio recording of a guitar. The method utilizes audio feature extraction and shallow neural networks, which are trained on data created specifically for this task. The results show that the method is generally suited for this task with average estimation errors of ±5% − ±16% of different parameter scales and could potentially perform near the level of a human expert.
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. This paper introduces a method to estimate the parameter settings of guitar effects, which makes it possible to reconstruct the effect and its settings from an audio recording of a guitar. The method utilizes audio feature extraction and shallow neural networks, which are trained on data created specifically for this task. The results show that the method is generally suited for this task with average estimation errors of ±5% − ±16% of different parameter scales and could potentially perform near the level of a human expert.
UR - http://www.scopus.com/inward/record.url?scp=85122559163&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85122559163
VL - 1
SP - 310
EP - 316
JO - Proceedings of the International Conference on Digital Audio Effects, DAFx
JF - Proceedings of the International Conference on Digital Audio Effects, DAFx
SN - 2413-6700
T2 - 23rd International Conference on Digital Audio Effects, eDAFx2020
Y2 - 9 September 2020 through 11 September 2020
ER -