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

2019 ◽  
Vol 54 (11) ◽  
pp. 3005-3016 ◽  
Author(s):  
Sechang Oh ◽  
Hun-Seok Kim ◽  
Dennis Sylvester ◽  
Minchang Cho ◽  
Zhan Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Acoustic Signal ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Neural Network Classification ◽  
Frequency Scanning ◽  
Voice Activity ◽  
Sequential Frequency

scholarly journals Voice activity detection in noisy conditions using tiny convolutional neural network

Informatics ◽  
2020 ◽  
Vol 17 (2) ◽  
pp. 36-43
Author(s):  
R. S. Vashkevich ◽  
E. S. Azarov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Voice Activity Detection ◽  
Detection Accuracy ◽  
Activity Detection ◽  
Detection Model ◽  
Proposed Model ◽  
Computational Resources ◽  
Voice Activity

The paper investigates the problem of voice activity detection from a noisy sound signal. An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. Proposed model doesn’t require a lot of computational resources that allows to use it as part of the “internet of things” concept for compact low power devices. At the same time the model provides state of the art results in voice activity detection in terms of detection accuracy. The properties of the model are achieved by using a special convolutional layer that considers the harmonic structure of vocal speech. This layer also eliminates redundancy of the model because it has invariance to changes of fundamental frequency. The model performance is evaluated in various noise conditions with different signal-to-noise ratios. The results show that the proposed model provides higher accuracy compared to voice activity detection model from the WebRTC framework by Google.

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