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

In this paper, we propose a joint generative and contrastive representationlearning method (GeCo) for anomalous sound detection (ASD). GeCo exploits aPredictive AutoEncoder (PAE) equipped with self-attention as a generative modelto perform frame-level prediction. The output of the PAE together with originalnormal samples, are used for supervised contrastive representative learning ina multi-task framework. Besides cross-entropy loss between classes, contrastiveloss is used to separate PAE output and original samples within each class.GeCo aims to better capture context information among frames, thanks to theself-attention mechanism for PAE model. Furthermore, GeCo combines generativeand contrastive learning from which we aim to yield more effective andinformative representations, compared to existing methods. Extensiveexperiments have been conducted on the DCASE2020 Task2 development dataset,showing that GeCo outperforms state-of-the-art generative and discriminativemethods.

Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection