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

Crackling signals cannot be identified using any sound level or quality metric that relies solely on the long-term spectrum as input. In order to identify sound quality metrics that might succeed in modeling human perception of crackling and non-crackling sounds a set of metrics sensitive to temporal properties of signals is applied to a set of signals with equivalent spectra but exhibiting varying degrees of crackle. Several methods for altering signals including some that remove crackling sound quality from an acoustic signal were drawn from previous work [Swift, Gee, Neilsen, 2014, Swift, Gee, Neilsen, 2017]. In this paper, an additional alteration which can partially remove crackle—randomizing the Fourier phase of a crackling signal in the frequency domain in selected frequency ranges—is considered. Variables from time-varying sound quality metrics such as loudness and sharpness, as well as roughness to signals exhibiting varying degrees of crackle are explored and relationships between them that can ...

Exploring the use of time-sensitive sound quality metrics and related quantities for detecting crackle