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Noise profiling for speech enhancement employing machine learning models.

Krzysztof Kąkol1, Gražina Korvel2, Bożena Kostek3

  • 1PGS Software, Wrocław, 50-086, Poland.

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Summary

This study introduces a machine learning (ML) method for real-time noise profiling. The Naive Bayes algorithm achieved 96.76% accuracy in identifying noise types, crucial for improving speech intelligibility.

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Acoustics

Background:

  • Effective noise profiling is essential for enhancing speech intelligibility in noisy environments.
  • Existing methods face challenges in real-time application and accurate noise identification.
  • Machine learning offers potential for developing advanced noise profiling techniques.

Approach:

  • A critical literature review was conducted to understand noise profiling challenges.
  • A noise recognition model was developed using various classifiers and features from the Aurora dataset.
  • The Naive Bayes classifier was selected for its superior performance (96.76% accuracy) in noise-type recognition.

Key Points:

  • The Naive Bayes model demonstrated stable performance in real-life noise recordings.
  • The study compared multiple classifiers, highlighting the effectiveness of Naive Bayes.
  • Accurate noise identification is critical for targeted noise reduction strategies.

Conclusions:

  • A novel, near real-time machine learning-based noise profiling method has been proposed.
  • The Naive Bayes algorithm is highly effective for noise-type recognition, enabling speech intelligibility enhancements.
  • Future work will focus on further refining the model and exploring broader applications.