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 the use of deep neural networks, it is crucial to provide appropriateinput representations for the network to learn from. In this paper, we proposean approach to learn a representation that focus on rhythmic representationwhich is named as DLR (Deep Learning Rhythmic representation). The proposedapproach aims to learn DLR from the raw audio signal and use it for other musicinformatics tasks. A 1-dimensional convolutional network is utilised in thelearning of DLR. In the experiment, we present the results from the source taskand the target task as well as visualisations of DLRs. The results reveals thatDLR provides compact rhythmic information which can be used on multi-taggingtask.

DLR : Toward a deep learned rhythmic representation for music content analysis