SEDA: A tunable Q-factor wavelet-based noise reduction algorithm for multi-talker babble

Roozbeh Soleymani, Ivan W. Selesnick, David M. Landsberger

Research output: Contribution to journalArticlepeer-review


We introduce a new wavelet-based algorithm to enhance the quality of speech corrupted by multi-talker babble noise. The algorithm comprises three stages: The first stage classifies short frames of the noisy speech as speech-dominated or noise-dominated. We design this classifier specifically for multi-talker babble noise. The second stage performs preliminary de-nosing of noisy speech frames using oversampled wavelet transforms and parallel group thresholding. The final stage performs further denoising by attenuating residual high frequency components in the signal produced by the second stage. A significant improvement in intelligibility and quality was observed in evaluation tests of the algorithm with cochlear implant users.

Original languageEnglish (US)
Pages (from-to)102-115
Number of pages14
JournalSpeech Communication
StatePublished - Feb 2018

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications


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