Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

01-08-2018 | Original Article | Issue 7/2019

International Journal of Machine Learning and Cybernetics 7/2019

A voice activity detection algorithm in spectro-temporal domain using sparse representation

Journal:
International Journal of Machine Learning and Cybernetics > Issue 7/2019
Authors:
Mohadese Eshaghi, Farbod Razzazi, Alireza Behrad
Important notes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

This paper describes a new algorithm for voice activity detection (VAD), based on sparse representation of spectro-temporal domain. Our audio classification algorithm is based on multi-scale spectro-temporal modulation features which are extracted using auditory cortex model. The key concept in sparse representation is that any speech fragment can be represented as a linear combination of a small number of exemplar speech tokens. In this algorithm, the approach transforms the speech into spectro-temporal domain resulting in its decomposition into auditory-based features with multiple scales of temporal and spectral resolutions; in the next stage, each frame is divided into several sub-cubes in the new domain; then the algorithm detects the speech in the signal by using the sparse representation of sub-cubes of the frames in this domain. Simulation results are given to illustrate the effectiveness of our new VAD algorithms. The results reveal that the achieved performance is 90.11 and 91.75% under − 5 db SNR in white and car noise respectively, outperforming most of the state of the art VAD algorithms.

Please log in to get access to this content

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 69.000 Bücher
  • über 500 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 50.000 Bücher
  • über 380 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Umwelt
  • Maschinenbau + Werkstoffe




Testen Sie jetzt 30 Tage kostenlos.

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 58.000 Bücher
  • über 300 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Testen Sie jetzt 30 Tage kostenlos.

Literature
About this article

Other articles of this Issue 7/2019

International Journal of Machine Learning and Cybernetics 7/2019 Go to the issue