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Related Experiment Video

Updated: May 12, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Automatic chick cough detection system based on improved audio spectrogram convolutional transformer neural network.

Bowen Cai1,2, Bo Zhou2,3, Xiangshuai Kong3

  • 1School of Environmental, Tsinghua University, Beijing, China.

Frontiers in Veterinary Science
|May 11, 2026
PubMed
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This summary is machine-generated.

This study introduces an acoustic detection system for chicken coughing (ASCT-CC) to monitor poultry health. The system uses an improved audio spectrogram transformer (AST) for early detection of respiratory diseases, achieving over 92% accuracy.

Area of Science:

  • Animal Science
  • Computer Science
  • Veterinary Medicine

Background:

  • Respiratory diseases pose a significant threat to poultry health, especially in high-density farming environments.
  • Rapid disease spread can lead to large-scale infections and economic losses.
  • Early detection and intervention are crucial for preventing disease outbreaks in poultry farms.

Purpose of the Study:

  • To develop an effective software system for monitoring and providing early warnings of respiratory diseases in chickens.
  • To create an acoustic detection system for chicken coughing (ASCT-CC) suitable for real-world poultry farming conditions.
  • To enhance poultry health and prevent disease spread through intelligent monitoring.

Main Methods:

  • An improved audio spectrogram transformer (AST) architecture with a hybrid convolutional-transformer backbone was employed.
Keywords:
audio spectrogram transformercough detectionedge computinglocal multi-head attentionpoultry sciencerespiratory disease monitoring

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  • Local multi-head attention replaced global attention for enhanced local acoustic information capture and noise robustness.
  • A two-branch co-learning structure, focal loss, and a connectionist temporal classification (CTC) decoder were utilized for accurate cough event identification and localization.
  • The system was deployed on edge computing devices using TensorRT acceleration and INT8 quantization for low-latency, real-time performance.

    • Main Results:

    • The proposed ASCT-CC system achieved a mean average precision (mAP) of 92.86% during training.
    • An independent test set yielded an identification rate of 92.11% for chicken cough events.
    • The system demonstrated a low inference time of approximately 200 milliseconds, enabling 24/7 real-time monitoring.

    Conclusions:

    • The ASCT-CC system offers effective technical support for the early detection and intelligent control of respiratory diseases in poultry.
    • The developed system provides multi-level early warning capabilities, crucial for timely disease management.
    • The system's efficiency and accuracy contribute to enhancing chicken health and farm biosecurity.