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arXiv · 2105.07031

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
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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.

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Shawn Hershey, Daniel P W Ellis, Eduardo Fonseca, Aren Jansen, Caroline Liu, R Channing Moore, Manoj Plakal. 2021-05-14. The Benefit Of Temporally-Strong Labels In Audio Event Classification. https://arxiv.org/abs/2105.07031

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