Voice Activity Detection for Transient Noisy Environment Based on Diffusion Nets

Amir Ivry, Baruch Berdugo, Israel Cohen

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying geometric structure. This process is done through a deep encoder-decoder-based neural network architecture. This structure involves an encoder that maps spectral features with temporal information to their low-dimensional representations, which are generated by applying the diffusion maps method. The encoder feeds a decoder that maps the embedded data back into the high-dimensional space. A deep neural network, which is trained to separate speech from non-speech frames, is obtained by concatenating the decoder to the encoder, resembling the known diffusion nets architecture. Experimental results show enhanced performance compared to competing voice activity detection methods. The improvement is achieved in both accuracy, robustness, and generalization ability. Our model performs in a real-time manner and can be integrated into audio-based communication systems. We also present a batch algorithm that obtains an even higher accuracy for offline applications.

Original languageEnglish
Article number8681421
Pages (from-to)254-264
Number of pages11
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number2
StatePublished - May 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2007-2012 IEEE.


  • Deep learning
  • diffusion maps
  • voice activity detection


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