The Benefit of Temporally-Strong Labels in Audio Event Classification
Shawn Hershey, Daniel P. W. Ellis, Eduardo Fonseca, Aren Jansen, Caroline Liu, R. Channing Moore, Manoj Plakal
Abstract
To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (∼0.1 sec resolution) "strong" labels for a portion of the AudioSet dataset. We devised a temporally-strong evaluation set (including explicit negatives of varying difficulty) and a small strong-labeled training subset of 67k clips (compared to the original dataset’s 1.8M clips labeled at 10 sec resolution). We show that fine-tuning with a mix of weak- and strongly-labeled data can substantially improve classifier performance, even when evaluated using only the original weak labels. For a ResNet-50 architecture, d′ on the strong evaluation data including explicit negatives improves from 1.13 to 1.39. The new labels are available as an update to AudioSet.
BibTeX
@inproceedings{icassp2021_thebenefitoftemp,
title = {The Benefit of Temporally-Strong Labels in Audio Event Classification},
author = {Shawn Hershey and Daniel P. W. Ellis and Eduardo Fonseca and Aren Jansen and Caroline Liu and R. Channing Moore and Manoj Plakal},
booktitle = {ICASSP 2021},
year = {2021}
}