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

Roozbeh Soleymani1,2, Ivan W Selesnick1, David M Landsberger2

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A new wavelet-based algorithm effectively reduces multi-talker babble noise in speech. This speech enhancement technology significantly improves clarity and quality, especially for cochlear implant users.

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Area of Science:

  • Signal Processing
  • Acoustics
  • Biomedical Engineering

Background:

  • Multi-talker babble noise significantly degrades speech quality and intelligibility.
  • Existing noise reduction methods often struggle with complex babble noise environments.
  • Cochlear implant users are particularly sensitive to background noise interference.

Purpose of the Study:

  • To develop and evaluate a novel wavelet-based algorithm for enhancing speech corrupted by multi-talker babble noise.
  • To improve the intelligibility and quality of degraded speech signals.
  • To assess the algorithm's effectiveness in a population with hearing impairments.

Main Methods:

  • A three-stage algorithm was developed, starting with a specialized classifier for multi-talker babble noise.
  • Stage one classifies speech frames as speech-dominated or noise-dominated.
  • Stages two and three employ oversampled wavelet transforms, parallel group thresholding, and residual high-frequency component attenuation for denoising.

Main Results:

  • The algorithm demonstrated significant improvements in speech intelligibility.
  • Objective and subjective evaluations showed enhanced speech quality.
  • Cochlear implant users reported a notable positive impact on their listening experience.

Conclusions:

  • The proposed wavelet-based algorithm offers an effective solution for speech enhancement in challenging multi-talker babble noise conditions.
  • The algorithm shows particular promise for improving auditory perception in cochlear implant users.
  • This approach represents a valuable advancement in assistive listening technologies.