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

Due to the variability in characteristics of audio scenes, some scenes cannaturally be recognized earlier than others. In this work, rather than usingequal-length snippets for all scene categories, as is common in the literature,we study to which temporal extent an audio scene can be reliably recognizedgiven state-of-the-art models. Moreover, as model fusion with deep networkensemble is prevalent in audio scene classification, we further study whether,and if so, when model fusion is necessary for this task. To achieve thesegoals, we employ two single-network systems relying on a convolutional neuralnetwork and a recurrent neural network for classification as well as earlyfusion and late fusion of these networks. Experimental results on theLITIS-Rouen dataset show that some scenes can be reliably recognized with a fewseconds while other scenes require significantly longer durations. In addition,model fusion is shown to be the most beneficial when the signal length isshort.

Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?