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

We describe in this report our audio scene recognition system submitted tothe DCASE 2016 challenge. Firstly, given the label set of the scenes, a labeltree is automatically constructed. This category taxonomy is then used in thefeature extraction step in which an audio scene instance is represented by alabel tree embedding image. Different convolutional neural networks, which aretailored for the task at hand, are finally learned on top of the image featuresfor scene recognition. Our system reaches an overall recognition accuracy of81.2% and 83.3% and outperforms the DCASE 2016 baseline with absoluteimprovements of 8.7% and 6.1% on the development and test data, respectively.

CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition