The Benefit Of Temporally-Strong Labels In Audio Event Classification
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 ResNet50 architecture, d' on the strong evaluation data including explicit negatives improves from 1.13 to 1.41. The new labels are available as an update to AudioSet.
Keywords
Cite
@article{arxiv.2105.07031,
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},
journal= {arXiv preprint arXiv:2105.07031},
year = {2021}
}
Comments
Accepted for publication at ICASSP 2021