| Paper ID | MLSP-11.3 |
| Paper Title |
UNSUPERVISED DISCRIMINATIVE LEARNING OF SOUNDS FOR AUDIO EVENT CLASSIFICATION |
| Authors |
Sascha Hornauer, Ke Li, Stella Yu, University of California, Berkeley, United States; Shabnam Ghaffarzadegan, Liu Ren, Robert Bosch LLC, United States |
| Session | MLSP-11: Self-supervised Learning for Speech Processing |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-SSUP] Self-supervised and semi-supervised learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transfer across different domains, training a model on large-scale visual datasets is time consuming. On several audio event classification benchmarks, we show a fast and effective alternative that pre-trains the model unsupervised, only on audio data and yet delivers on-par performance with ImageNet pre-training. Furthermore, we show that our discriminative audio learning can be used to transfer knowledge across audio datasets and optionally include ImageNet pre-training. |
