Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Title | Unsupervised Discriminative Learning of Sounds for Audio Event Classification |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Hornauer, S., Li K., Yu S. X., Ghaffarzadegan S., & Ren L. |
Published in | Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing |
Date Published | 06/2021 |
Publisher | IEEE |
Other Numbers | arXiv:2105.09279 |
Keywords | audio event classification, unsupervised representation learning |
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. |
URL | http://www1.icsi.berkeley.edu/~stellayu/publication/doc/2021audioICASSP.pdf |
DOI | 10.1109/ICASSP39728.2021.9413482 |