SVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks-Reference-Cited by-同舟云学术

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SVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks

Published:2024-04-14 Issue: Volume:35 Page:221-225
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Yang Qu¹,Liu Qianhui¹,Li Nan²,Ge Meng¹,Song Zeyang¹,Li Haizhou³

Affiliation:

1. National University of Singapore,Singapore

2. Tianjin University,Tianjin,China

3. The Chinese University of Hong Kong,Shenzhen (CUHK-Shenzhen),China

Funder

National University of Singapore

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10445798/10445803/10446945.pdf?arnumber=10446945

Reference30 articles.

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

2. A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks

3. HuRAI: A brain-inspired computational model for human-robot auditory interface

4. Hardware Implementations for Voice Activity Detection: Trends, Challenges and Outlook

5. Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks

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