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

Automatic coded audio quality assessment is an important task whose progressis hampered by the scarcity of human annotations, poor generalization to unseencodecs, bitrates, content-types, and a lack of flexibility of existingapproaches. One of the typical human-perception-related metrics, ViSQOL v3(ViV3), has been proven to provide a high correlation to the quality scoresrated by humans. In this study, we take steps to tackle problems of predictingcoded audio quality by completely utilizing programmatically generated datathat is informed with expert domain knowledge. We propose a learnable neuralnetwork, entitled InSE-NET, with a backbone of Inception andSqueeze-and-Excitation modules to assess the perceived quality of coded audioat a 48kHz sample rate. We demonstrate that synthetic data augmentation iscapable of enhancing the prediction. Our proposed method is intrusive, i.e. itrequires Gammatone spectrograms of unencoded reference signals. Besides acomparable performance to ViV3, our approach provides a more robust predictiontowards higher bitrates.

InSE-NET: A Perceptually Coded Audio Quality Model based on CNN