Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Artificial Intelligence Applications and Innovations |
| Untertitel | 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II |
| Herausgeber/-innen | Ilias Maglogiannis, Lazaros Iliadis, John Macintyre, Paulo Cortez |
| Erscheinungsort | Cham |
| Seiten | 426-433 |
| Seitenumfang | 8 |
| Auflage | 1. |
| ISBN (elektronisch) | 978-3-031-08337-2 |
| Publikationsstatus | Veröffentlicht - 10 Juni 2022 |
| Extern publiziert | Ja |
Publikationsreihe
| Name | IFIP Advances in Information and Communication Technology |
|---|---|
| Band | 647 |
| ISSN (Print) | 1868-4238 |
| ISSN (elektronisch) | 1868-422X |
Abstract
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is using transfer learning, is quick to train and the results demonstrate performance comparable to other state-of-the-art approaches that require a lot more compute resources, training data, and time. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/unmix ).
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Artificial Intelligence Applications and Innovations : 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II. Hrsg. / Ilias Maglogiannis; Lazaros Iliadis; John Macintyre; Paulo Cortez. 1. Aufl. Cham, 2022. S. 426-433 (IFIP Advances in Information and Communication Technology; Band 647).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Transfer Learning with Jukebox for Music Source Separation
AU - Zai El Amri, Wadhah
AU - Tautz, Oliver
AU - Ritter, Helge
AU - Melnik, Andrew
N1 - Publisher Copyright: © 2022, IFIP International Federation for Information Processing.
PY - 2022/6/10
Y1 - 2022/6/10
N2 - In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is using transfer learning, is quick to train and the results demonstrate performance comparable to other state-of-the-art approaches that require a lot more compute resources, training data, and time. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/unmix ).
AB - In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel. Our neural network architecture, which is using transfer learning, is quick to train and the results demonstrate performance comparable to other state-of-the-art approaches that require a lot more compute resources, training data, and time. We provide an open-source code implementation of our architecture (https://github.com/wzaielamri/unmix ).
UR - http://www.scopus.com/inward/record.url?scp=85133305158&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-08337-2_35
DO - 10.1007/978-3-031-08337-2_35
M3 - Conference contribution
SN - 978-3-031-08336-5
SN - 978-3-031-08339-6
T3 - IFIP Advances in Information and Communication Technology
SP - 426
EP - 433
BT - Artificial Intelligence Applications and Innovations
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Macintyre, John
A2 - Cortez, Paulo
CY - Cham
ER -