ICASSP 2021accepted0 citations
Unsupervised Discriminative Learning of Sounds for Audio Event Classification
Sascha Hornauer, Ke Li, Stella X. Yu, Shabnam Ghaffarzadegan, Liu Ren
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.
BibTeX
@inproceedings{icassp2021_unsuperviseddisc,
title = {Unsupervised Discriminative Learning of Sounds for Audio Event Classification},
author = {Sascha Hornauer and Ke Li and Stella X. Yu and Shabnam Ghaffarzadegan and Liu Ren},
booktitle = {ICASSP 2021},
year = {2021}
}