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

Classical speech coding uses low-complexity postfilters with zero lookaheadto enhance the quality of coded speech, but their effectiveness is limited bytheir simplicity. Deep Neural Networks (DNNs) can be much more effective, butrequire high complexity and model size, or added delay. We propose a DNN modelthat generates classical filter kernels on a per-frame basis with a model ofjust 300~K parameters and 100~MFLOPS complexity, which is a practicalcomplexity for desktop or mobile device CPUs. The lack of added delay allows itto be integrated into the Opus codec, and we demonstrate that it enableseffective wideband encoding for bitrates down to 6 kb/s.

LACE: A light-weight, causal model for enhancing coded speech through adaptive convolutions