Loading [a11y]/accessibility-menu.js
An Acoustic Signal Processing Chip With 142-nW Voice Activity Detection Using Mixer-Based Sequential Frequency Scanning and Neural Network Classification | IEEE Journals & Magazine | IEEE Xplore

An Acoustic Signal Processing Chip With 142-nW Voice Activity Detection Using Mixer-Based Sequential Frequency Scanning and Neural Network Classification


Abstract:

This article presents a voice and acoustic activity detector that uses a mixer-based architecture and ultra-low-power neural network (NN)-based classifier. By sequentiall...Show More

Abstract:

This article presents a voice and acoustic activity detector that uses a mixer-based architecture and ultra-low-power neural network (NN)-based classifier. By sequentially scanning 4 kHz of frequency bands and down-converting to below 500 Hz, feature extraction power consumption is reduced by 4×. The NN processor employs computational sprinting, enabling 12× power reduction. The system also features inaudible acoustic signature detection for intentional remote silent wakeup of the system while re-using a subset of the same system components. The measurement results achieve 91.5%/90% speech/non-speech hit rates at 10-dB SNR with babble noise and 142-nW power consumption. Acoustic signature detection consumes 66 nW, successfully detecting a signature 10 dB below the noise level.
Published in: IEEE Journal of Solid-State Circuits ( Volume: 54, Issue: 11, November 2019)
Page(s): 3005 - 3016
Date of Publication: 12 September 2019

ISSN Information:

Funding Agency:


References

References is not available for this document.