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 work we present a new single-microphone speech dereverberationalgorithm. First, a performance analysis is presented to interpret thatalgorithms focused on improving solely magnitude or phase are not good enough.Furthermore, we demonstrate that few objective measurements have highcorrelation with the clean magnitude while others with the clean phase.Consequently ,we propose a new architecture which consists of two sub-models,each of which is responsible for a different task. The first model estimatesthe clean magnitude given the noisy input. The enhanced magnitude together withthe noisy-input phase are then used as inputs to the second model to estimatethe real and imaginary portions of the dereverberated signal. A training schemeincluding pre-training and fine-tuning is presented in the paper. We evaluateour proposed approach using data from the REVERB challenge and compare ourresults to other methods. We demonstrate consistent improvements in allmeasures, which can be attributed to the improved estimates of both themagnitude and the phase.

Magnitude or Phase? A Two Stage Algorithm for Dereverberation