Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 9 |
| Seitenumfang | 17 |
| Fachzeitschrift | Eurasip Journal on Audio, Speech, and Music Processing |
| Jahrgang | 2024 |
| Ausgabenummer | 9 |
| Publikationsstatus | Veröffentlicht - 7 Feb. 2024 |
Abstract
Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. Automatic extraction of guitar effects and their parameter settings, with the aim to copy a target guitar sound, has been previously investigated, where artificial neural networks first determine the effect class of a reference signal and subsequently the parameter settings. These approaches require a corresponding guitar effect implementation to be available. In general, for very close sound matching, additional research regarding effect implementations is necessary. In this work, we present a different approach to circumvent these issues. We propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modeling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the phaser, softclipping and slapback delay effect. Listening tests with eight subjects indicate excellent quality of the blind copies, i.e., little to no difference to the reference guitar effect.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Akustik und Ultraschall
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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in: Eurasip Journal on Audio, Speech, and Music Processing, Jahrgang 2024, Nr. 9, 9, 07.02.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Blind extraction of guitar effects through blind system inversion and neural guitar effect modeling
AU - Hinrichs, Reemt
AU - Gerkens, Kevin
AU - Lange, Alexander
AU - Ostermann, Jörn
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. The research has not been funded by third parties.
PY - 2024/2/7
Y1 - 2024/2/7
N2 - Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. Automatic extraction of guitar effects and their parameter settings, with the aim to copy a target guitar sound, has been previously investigated, where artificial neural networks first determine the effect class of a reference signal and subsequently the parameter settings. These approaches require a corresponding guitar effect implementation to be available. In general, for very close sound matching, additional research regarding effect implementations is necessary. In this work, we present a different approach to circumvent these issues. We propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modeling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the phaser, softclipping and slapback delay effect. Listening tests with eight subjects indicate excellent quality of the blind copies, i.e., little to no difference to the reference guitar effect.
AB - Audio effects are an ubiquitous tool in music production due to the interesting ways in which they can shape the sound of music. Guitar effects, the subset of all audio effects focusing on guitar signals, are commonly used in popular music to shape the guitar sound to fit specific genres or to create more variety within musical compositions. Automatic extraction of guitar effects and their parameter settings, with the aim to copy a target guitar sound, has been previously investigated, where artificial neural networks first determine the effect class of a reference signal and subsequently the parameter settings. These approaches require a corresponding guitar effect implementation to be available. In general, for very close sound matching, additional research regarding effect implementations is necessary. In this work, we present a different approach to circumvent these issues. We propose blind extraction of guitar effects through a combination of blind system inversion and neural guitar effect modeling. That way, an immediately usable, blind copy of the target guitar effect is obtained. The proposed method is tested with the phaser, softclipping and slapback delay effect. Listening tests with eight subjects indicate excellent quality of the blind copies, i.e., little to no difference to the reference guitar effect.
KW - Blind system identification
KW - Demucs
KW - Guitar effect extraction
KW - Neural effect modeling
UR - http://www.scopus.com/inward/record.url?scp=85184856318&partnerID=8YFLogxK
U2 - 10.1186/s13636-024-00330-0
DO - 10.1186/s13636-024-00330-0
M3 - Article
AN - SCOPUS:85184856318
VL - 2024
JO - Eurasip Journal on Audio, Speech, and Music Processing
JF - Eurasip Journal on Audio, Speech, and Music Processing
SN - 1687-4714
IS - 9
M1 - 9
ER -