Adversarial Guitar Amplifier Modelling with Unpaired Data

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

6 Sitaatiot (Scopus)
48 Lataukset (Pure)

Abstrakti

We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording. We train a deep neural network using an adversarial approach, with the goal of trans-forming the timbre of a guitar, into the timbre of another guitar after audio effects processing has been applied, for example, by a guitar amplifier. The model training requires no paired data, and the resulting model emulates the target timbre well whilst being capable of real-time processing on a modern personal computer. To verify our approach we present two experiments, one which carries out un-paired training using paired data, allowing us to monitor training via objective metrics, and another that uses fully unpaired data, corresponding to a realistic scenario where a user wants to emulate a guitar timbre only using audio data from a recording. Our listening test results confirm that the models are perceptually convincing.
AlkuperäiskieliEnglanti
OtsikkoICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
KustantajaIEEE
Sivut1-5
Sivumäärä5
ISBN (elektroninen)978-1-7281-6327-7
ISBN (painettu)978-1-7281-6328-4
DOI - pysyväislinkit
TilaJulkaistu - 10 kesäk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Rhodes Island, Kreikka
Kesto: 4 kesäk. 202310 kesäk. 2023

Julkaisusarja

NimiProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
Maa/AlueKreikka
KaupunkiRhodes Island
Ajanjakso04/06/202310/06/2023

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