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

Modern smartphones possess hardware for audio acquisition and to performspeech processing tasks such as speaker recognition and health assessment.However, energy consumption remains a concern, especially forresource-intensive DNNs. Prior work has improved the DNN energy efficiency byutilizing a compact model or reducing the dimensions of speech features. Bothapproaches reduced energy consumption during DNN inference but not duringspeech acquisition. This paper proposes using a masking kernel integrated intogradient descent during DNN training to learn the most energy-efficient speechlength and sampling rate for windowing, a common step for sample construction.To determine the most energy-optimal parameters, a masking function withnon-zero derivatives was combined with a low-pass filter. The proposed approachminimizes the energy consumption of both data collection and inference by 57%,and is competitive with speaker recognition and traumatic brain injurydetection baselines.

Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health