  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.

Sound;Audio and Speech Processing