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, we propose a differentiable WORLD synthesizer and demonstrateits use in end-to-end audio style transfer tasks such as (singing) voiceconversion and the DDSP timbre transfer task. Accordingly, our baselinedifferentiable synthesizer has no model parameters, yet it yields adequatesynthesis quality. We can extend the baseline synthesizer by appendinglightweight black-box postnets which apply further processing to the baselineoutput in order to improve fidelity. An alternative differentiable approachconsiders extraction of the source excitation spectrum directly, which canimprove naturalness albeit for a narrower class of style transfer applications.The acoustic feature parameterization used by our approaches has the addedbenefit that it naturally disentangles pitch and timbral information so thatthey can be modeled separately. Moreover, as there exists a robust means ofestimating these acoustic features from monophonic audio sources, it allows forparameter loss terms to be added to an end-to-end objective function, which canhelp convergence and/or further stabilize (adversarial) training.

Differentiable WORLD Synthesizer-based Neural Vocoder With Application To End-To-End Audio Style Transfer