Details
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence Applications and Innovations |
| Subtitle of host publication | 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II |
| Editors | Ilias Maglogiannis, Lazaros Iliadis, John Macintyre, Paulo Cortez |
| Place of Publication | Cham |
| Pages | 426-433 |
| Number of pages | 8 |
| Edition | 1. |
| ISBN (electronic) | 978-3-031-08337-2 |
| Publication status | Published - 10 Jun 2022 |
| Externally published | Yes |
Publication series
| Name | IFIP Advances in Information and Communication Technology |
|---|---|
| Volume | 647 |
| ISSN (Print) | 1868-4238 |
| ISSN (electronic) | 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. ed. / Ilias Maglogiannis; Lazaros Iliadis; John Macintyre; Paulo Cortez. 1. ed. Cham, 2022. p. 426-433 (IFIP Advances in Information and Communication Technology; Vol. 647).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -