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A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection.
Sehgal, Abhishek; Kehtarnavaz, Nasser.
Afiliação
  • Sehgal A; Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.
  • Kehtarnavaz N; Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.
IEEE Access ; 6: 9017-9026, 2018.
Article em En | MEDLINE | ID: mdl-30250774
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: IEEE Access Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies Idioma: En Revista: IEEE Access Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos