Can I read Unsupervised Discriminative Learning of Sounds for Audio Event Classification on EtoBox?

Unsupervised Discriminative Learning of Sounds for Audio Event Classification by Hornauer, Sascha; Li, Ke; Yu, Stella X.; Ghaffarzadegan, Shabnam; Ren, Liu is a scholarly article available to read on EtoBox.

What is Unsupervised Discriminative Learning of Sounds for Audio Event Classification about?

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.

Author

Hornauer, Sascha; Li, Ke; Yu, Stella X.; Ghaffarzadegan, Shabnam; Ren, Liu

Published

2021

Language

EN