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Fused Audio Instance and Representation for Respiratory Disease Detection.

Tuan Truong1, Matthias Lenga1, Antoine Serrurier2

  • 1Bayer AG, 13353 Berlin, Germany.

Sensors (Basel, Switzerland)
|October 16, 2024
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Summary

This study introduces a novel audio classification method, FAIR, for respiratory disease detection. Combining cough, breath, and speech sounds significantly improves diagnostic accuracy for COVID-19.

Keywords:
COVID-19audioclassificationdeep learningmulti-instance learningrespiratory diseasespectrogramwaveform

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Area of Science:

  • Medical Informatics
  • Biomedical Signal Processing
  • Machine Learning for Healthcare

Background:

  • Audio-based classification of body sounds is crucial for diagnosing respiratory diseases.
  • While coughs are primary biomarkers, breath and speech sounds also show potential, especially for COVID-19 detection.

Purpose of the Study:

  • To propose and evaluate the Fused Audio Instance and Representation (FAIR) method for enhanced respiratory disease detection.
  • To investigate the efficacy of combining diverse audio representations (waveform and spectrogram) and body sounds (cough, breath, speech).

Main Methods:

  • Developed FAIR, a method constructing joint feature vectors from multiple body sound representations.
  • Utilized self-attention mechanisms to integrate features extracted from cough, breath, and speech sounds.
  • Experimented with combining waveform and spectrogram data for COVID-19 detection.

Main Results:

  • The FAIR method achieved an Area Under the Curve (AUC) of 0.8658, with 0.8057 sensitivity and 0.7958 specificity for COVID-19 detection.
  • Combining both waveform and spectrogram representations significantly improved AUC scores compared to single-representation models.
  • Self-attention integration of cough, breath, and speech sound features yielded the highest performance.

Conclusions:

  • FAIR demonstrates superior performance in respiratory disease detection by fusing multi-modal audio features.
  • Combining waveform and spectrogram representations enriches feature extraction, outperforming single-representation approaches.
  • The FAIR method's flexibility supports broader applications in multi-modal diagnostic systems for various diseases.