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Comparing Binaural Pre-processing Strategies I: Instrumental Evaluation.

Regina M Baumgärtel1, Martin Krawczyk-Becker2, Daniel Marquardt3

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Summary
This summary is machine-generated.

This study evaluated eight noise reduction algorithms for speech intelligibility and sound quality in various noisy environments. Binaural beamforming algorithms showed significant improvements across all tested scenarios.

Keywords:
audio qualitybinauralinstrumental evaluationsignal enhancementspeech intelligibility

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Area of Science:

  • Acoustics
  • Signal Processing
  • Speech Technology

Background:

  • Noise reduction algorithms are crucial for improving speech intelligibility in challenging acoustic environments.
  • Evaluating these algorithms requires objective measures of speech intelligibility and sound quality.

Purpose of the Study:

  • To comprehensively evaluate eight monaural and binaural noise reduction algorithms.
  • To assess their performance in realistic, reverberant listening conditions using instrumental measures.

Main Methods:

  • Utilized four distinct reverberant scenarios: speech-shaped noise, multitalker babble, single interfering talker, and cafeteria noise.
  • Employed three instrumental measures: intelligibility-weighted signal-to-noise ratio (iSNR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ).

Main Results:

  • Significant improvements in predicted speech intelligibility and sound quality were observed for several algorithms.
  • A coherence-based noise reduction algorithm improved predicted audio signal quality.
  • Single-channel algorithms showed iSNR improvements except in cafeteria noise.
  • Binaural minimum variance distortionless response (MVDR) beamforming algorithms performed exceptionally well across all noise types.

Conclusions:

  • Noise reduction algorithms, particularly binaural MVDR beamforming, can substantially enhance speech intelligibility and sound quality.
  • Objective instrumental measures provide reliable assessment of algorithm performance in diverse acoustic conditions.