English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To reveal the importance of temporal precision in ground truth audio eventlabels, we collected precise (~0.1 sec resolution) "strong" labels for aportion of the AudioSet dataset. We devised a temporally strong evaluation set(including explicit negatives of varying difficulty) and a small strong-labeledtraining subset of 67k clips (compared to the original dataset's 1.8M clipslabeled at 10 sec resolution). We show that fine-tuning with a mix of weak andstrongly labeled data can substantially improve classifier performance, evenwhen evaluated using only the original weak labels. For a ResNet50architecture, d' on the strong evaluation data including explicit negativesimproves from 1.13 to 1.41. The new labels are available as an update toAudioSet.

The Benefit Of Temporally-Strong Labels In Audio Event Classification