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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Time-frequency masking or spectrum prediction computed via short symmetricwindows are commonly used in low-latency deep neural network (DNN) based sourceseparation. In this paper, we propose the usage of an asymmetricanalysis-synthesis window pair which allows for training with targets withbetter frequency resolution, while retaining the low-latency during inferencesuitable for real-time speech enhancement or assisted hearing applications. Inorder to assess our approach across various model types and datasets, weevaluate it with both speaker-independent deep clustering (DC) model and aspeaker-dependent mask inference (MI) model. We report an improvement inseparation performance of up to 1.5 dB in terms of source-to-distortion ratio(SDR) while maintaining an algorithmic latency of 8 ms.

Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair