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Multi-sensor neural-network processing of noisy speech.

A Hussain1

  • 1Department of Applied Computing, University of Dundee, Scotland, UK.

International Journal of Neural Systems
|January 12, 2000
PubMed
Summary
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This study introduces a novel Artificial Neural Network (ANN) system for speech enhancement. The ANN system effectively improves speech clarity in noisy and reverberant conditions, outperforming traditional methods.

Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Acoustics

Background:

  • Speech signals are often corrupted by real-world noise and reverberation.
  • Conventional noise-cancellation schemes have limitations in effectively enhancing degraded speech.

Purpose of the Study:

  • To develop and evaluate a novel Artificial Neural Network (ANN) based adaptive signal-processing scheme for speech enhancement.
  • To improve the performance of speech enhancement in the presence of noise and reverberation.

Main Methods:

  • A multi-sensor, multi-band adaptive signal-processing scheme utilizing Artificial Neural Networks (ANNs) was developed.
  • Numerically robust adaptation algorithms were employed for ANN-based sub-band filters.
  • Simulation experiments were conducted using real-reverberant automobile data.

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Main Results:

  • The proposed ANN-based speech enhancement system demonstrated superior performance compared to conventional methods.
  • The system effectively enhanced acoustic-speech corrupted by real noise and reverberation.
  • The multi-band adaptive filtering approach showed significant improvements.

Conclusions:

  • The novel ANN-based adaptive signal-processing scheme offers a promising approach for effective speech enhancement.
  • This system outperforms traditional linear filtering techniques in challenging acoustic environments.
  • The findings highlight the potential of ANNs in advanced audio signal processing.