A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection

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Authors Abhishek Sehgal, Nasser Kehtarnavaz
Journal/Conference Name IEEE Access 2018 2
Paper Category
Paper Abstract This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated with convolutional neural networks is addressed. The developed smartphone app is meant to act as a switch for noise reduction in the signal processing pipelines of hearing devices, enabling noise estimation or classification to be conducted in noise-only parts of noisy speech signals. The developed smartphone app is compared with a previously developed voice activity detection app as well as with two highly cited voice activity detection algorithms. The experimental results indicate that the developed app using convolutional neural network outperforms the previously developed smartphone app.
Date of publication 2018
Code Programming Language Jupyter Notebook

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