Analog Acoustic Feature Extraction and Delta-Sigma Modulation based Neural Network Classification for Voice Activity Detection
Author:
Affiliation:
1. Indian Institute of Technology,Department of Electrical Engineering,Bombay,India,400076
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10038981/10039721/10039873.pdf?arnumber=10039873
Reference10 articles.
1. A 28.5µW All-Analog Voice-Activity Detector;mukherjee;2021 IEEE International Symposium on Circuits and Systems (ISCAS),2021
2. An 800 nW Switched-Capacitor Feature Extraction Filterbank for Sound Classification
3. A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting
4. Design of an Always-On Deep Neural Network-Based 1-$\mu$ W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction
5. A 90 nm CMOS, 6 µW Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection;badami;IEEE Journal of Solid-State Circuits,2016
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