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

We present a novel model designed for resource-efficient multichannel speechenhancement in the time domain, with a focus on low latency, lightweight, andlow computational requirements. The proposed model incorporates explicitspatial and temporal processing within deep neural network (DNN) layers.Inspired by frequency-dependent multichannel filtering, our spatial filteringprocess applies multiple trainable filters to each hidden unit across thespatial dimension, resulting in a multichannel output. The temporal processingis applied over a single-channel output stream from the spatial processingusing a Long Short-Term Memory (LSTM) network. The output from the temporalprocessing stage is then further integrated into the spatial dimension throughelementwise multiplication. This explicit separation of spatial and temporalprocessing results in a resource-efficient network design. Empirical findingsfrom our experiments show that our proposed model significantly outperformsrobust baseline models while demanding far fewer parameters and computations,while achieving an ultra-low algorithmic latency of just 2 milliseconds.

Decoupled Spatial and Temporal Processing for Resource Efficient Multichannel Speech Enhancement