A 90 nm CMOS, 6 μW Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection

This work presents a sub-6 µW acoustic front-end for speech/non-speech classification in a voice activity detection (VAD) in 90 nm CMOS. Power consumption of the VAD system is minimized by architectural design around a new Power-Proportional sensing paradigm and the use of machine-learning assisted moderate-precision analog analytics for classification. Power-Proportional sensing allows for hierarchical and context-aware scaling of the frontend’s power consumption depending on the complexity of the ongoing information extraction, while the use of analog analytics brings increased power efficiency through switching on/off the computation of individual features depending on the features’ usefulness in a particular context. The proposed VAD system reduces the power consumption by 10X as compared to state-of-the-art systems and yet achieves an 89% average hit rate for a 12 dB signal to acoustic noise ratio in babble context, which is at par with software based VAD systems.

Jaar van publicatie:2016

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:3

CSS-citation score:3

Authors from:Higher Education

Toegankelijkheid:Open