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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Existing singing voice synthesis models (SVS) are usually trained on singingdata and depend on either error-prone time-alignment and duration features orexplicit music score information. In this paper, we propose Karaoker, amultispeaker Tacotron-based model conditioned on voice characteristic featuresthat is trained exclusively on spoken data without requiring time-alignments.Karaoker synthesizes singing voice and transfers style following amulti-dimensional template extracted from a source waveform of an unseensinger/speaker. The model is jointly conditioned with a single deepconvolutional encoder on continuous data including pitch, intensity,harmonicity, formants, cepstral peak prominence and octaves. We extend thetext-to-speech training objective with feature reconstruction, classificationand speaker identification tasks that guide the model to an accurate result. Inaddition to multitasking, we also employ a Wasserstein GAN training scheme aswell as new losses on the acoustic model's output to further refine the qualityof the model.

Karaoker: Alignment-free singing voice synthesis with speech training data