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, a siamese DNN model is proposed to learn the characteristicsof the audio dynamic range compressor (DRC). This facilitates an intelligentcontrol system that uses audio examples to configure the DRC, a widely usednon-linear audio signal conditioning technique in the areas of musicproduction, speech communication and broadcasting. Several alternative siameseDNN architectures are proposed to learn feature embeddings that cancharacterise subtle effects due to dynamic range compression. These models arecompared with each other as well as handcrafted features proposed in previouswork. The evaluation of the relations between the hyperparameters of DNN andDRC parameters are also provided. The best model is able to produce a universalfeature embedding that is capable of predicting multiple DRC parameterssimultaneously, which is a significant improvement from our previous research.The feature embedding shows better performance than handcrafted audio featureswhen predicting DRC parameters for both mono-instrument audio loops andpolyphonic music pieces.

A Feature Learning Siamese Model for Intelligent Control of the Dynamic Range Compressor