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A multi-branch convolutional neural network for snoring detection based on audio.

Hao Dong1,2, Haitao Wu2,3, Guan Yang1

  • 1School of Computer Science, Zhongyuan University of Technology, Henan, China.

Computer Methods in Biomechanics and Biomedical Engineering
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

A new multi-branch convolutional neural network (MBCNN) accurately detects snoring using audio analysis. This AI model achieves 99.5% accuracy, offering a significant advancement in identifying snoring events for sleep medicine.

Keywords:
Obstructive sleep apneaconvolutional neural networkdeep learningmulti-scale featuressnore detection

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence
  • Audio Signal Processing

Background:

  • Obstructive sleep apnea (OSA) is linked to health issues, with snoring as a key symptom.
  • Effective snoring detection is vital for sleep medicine.
  • Audio analysis provides a convenient method for snoring identification.

Purpose of the Study:

  • To develop a convolutional neural network (CNN) for classifying snoring and non-snoring events from audio data.
  • To enhance snoring detection accuracy using advanced AI techniques.

Main Methods:

  • Utilized Mel-frequency cepstral coefficients (MFCCs) for audio feature extraction.
  • Proposed a multi-branch convolutional neural network (MBCNN) for multi-scale feature extraction.
  • Employed asymmetric convolutional kernels and one-hot encoding for improved classification.

Main Results:

  • The MBCNN achieved a snoring detection accuracy of 99.5% on a public dataset.
  • Integration of multi-scale features significantly improved snoring classification performance.
  • The developed model demonstrated high efficacy in distinguishing snoring from non-snoring sounds.

Conclusions:

  • The MBCNN model offers a highly accurate and effective method for snoring detection based on audio analysis.
  • This AI-driven approach represents a substantial improvement in classifying snoring events.
  • The findings support the use of audio analysis and MBCNN for sleep medicine applications.