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

Singing voice separation aims to separate music into vocals and accompanimentcomponents. One of the major constraints for the task is the limited amount oftraining data with separated vocals. Data augmentation techniques such asrandom source mixing have been shown to make better use of existing data andmildly improve model performance. We propose a novel data augmentationtechnique, chromagram-based pitch-aware remixing, where music segments withhigh pitch alignment are mixed. By performing controlled experiments in bothsupervised and semi-supervised settings, we demonstrate that training modelswith pitch-aware remixing significantly improves the test signal-to-distortionratio (SDR)

Improved singing voice separation with chromagram-based pitch-aware remixing