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Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion.

Vahid Hajihashemi1, Abdorreza Alavi Gharahbagh1, Pedro Miguel Cruz2

  • 1Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.

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Summary

This study introduces a hybrid approach for acoustic scene classification (ASC) using stereo audio signals and a novel fusion technique. The method significantly improves sound event classification accuracy, achieving approximately 95% on the DCASE 2017 dataset.

Keywords:
genetic algorithmmachine learningsound base intelligent systemstereo signalurban sounds classification

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Machine Learning

Background:

  • Ambient sound analysis is crucial for intelligent systems.
  • Acoustic Scene Classification (ASC) identifies sound environments.
  • Current ASC models face challenges in accuracy and adaptability.

Purpose of the Study:

  • To develop a hybrid method for enhanced ASC accuracy and adaptability.
  • To introduce a novel mathematical fusion step for stereo audio signals.
  • To improve urban sound event classification systems.

Main Methods:

  • Utilized stereo audio signals and Wavelet Scattering Transform (WST) for stable signal representation.
  • Employed two ensemble classifiers (random subspace) trained on WST features for each audio channel.
  • Developed a novel mathematical fusion formula with parameters optimized by a Genetic Algorithm.

Main Results:

  • Achieved high classification accuracy of approximately 95% on the DCASE 2017 dataset.
  • Demonstrated superior performance compared to existing state-of-the-art ASC methods.
  • Validated the effectiveness of the hybrid approach and the novel fusion technique.

Conclusions:

  • The proposed hybrid method significantly advances the accuracy and adaptability of acoustic scene classification.
  • The novel mathematical fusion step is key to the method's enhanced performance.
  • This research pushes the boundaries of ASC, offering potential for improved urban sound analysis systems.