Keep what you need : extracting efficient subnetworks from large audio representation models
- DOI
- 10.1109/icassp49660.2025.10887836
- Published
- 2025-04-06
- Container
- ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/icassp49660.2025.10887836,
title = {Keep what you need : extracting efficient subnetworks from large audio representation models},
author = {David Genova and Philippe Esling and Tom Hurlin},
year = {2025},
journal = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/icassp49660.2025.10887836},
url = {https://doi.org/10.1109/icassp49660.2025.10887836}
}RIS
TY - JOUR TI - Keep what you need : extracting efficient subnetworks from large audio representation models AU - David Genova AU - Philippe Esling AU - Tom Hurlin PY - 2025 JO - ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) DO - 10.1109/icassp49660.2025.10887836 UR - https://doi.org/10.1109/icassp49660.2025.10887836 ER -
APA
Genova, D., Esling, P., & Hurlin, T. (2025). Keep what you need : extracting efficient subnetworks from large audio representation models. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp49660.2025.10887836
Source records
- crossref · retrieved 2026-09-26T11:03:31.196Z