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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Sound event detection is to infer the event by understanding the surroundingenvironmental sounds. Due to the scarcity of rare sound events, it becomeschallenging for the well-trained detectors which have learned too much priorknowledge. Meanwhile, few-shot learning methods promise a good generalizationability when facing a new limited-data task. Recent approaches have achievedpromising results in this field. However, these approaches treat each supportexample independently, ignoring the information of other examples from thewhole task. Because of this, most of previous methods are constrained togenerate a same feature embedding for all test-time tasks, which is notadaptive to each inputted data. In this work, we propose a novel task-adaptivemodule which is easy to plant into any metric-based few-shot learningframeworks. The module could identify the task-relevant feature dimension.Incorporating our module improves the performance considerably on two datasetsover baseline methods, especially for the transductive propagation network.Such as +6.8% for 5-way 1-shot accuracy on ESC-50, and +5.9% on noiseESC-50. Weinvestigate our approach in the domain-mismatch setting and also achieve betterresults than previous methods.

Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection