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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Audio-based cough counting using independent subspace analysis.

Paul Leamy, Ted Burke, Dan Barry

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

This summary is machine-generated.

This study introduces an automated algorithm for detecting cough events in audio recordings, reducing manual counting time. The novel method uses time-frequency analysis and independent subspace analysis (ISA) for accurate cough detection without pre-training.

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Area of Science:

Computational acoustics
Signal processing
Biomedical engineering

Background:

Manual counting of cough events in audio recordings is time-consuming and labor-intensive.
Existing automated methods often require pre-trained models, limiting their adaptability.
Ambulatory cough monitoring is crucial for diagnosing and managing respiratory conditions.

Purpose of the Study:

To develop and evaluate an algorithm for automatic detection of characteristic cough events in audio recordings.
To reduce the time and effort associated with manual cough event analysis.
To provide a cough detection method that does not rely on pre-trained models.

Main Methods:

Utilized time-frequency representations of audio signals.
Employed independent subspace analysis (ISA) to identify cough-specific sound characteristics.
Tested the algorithm on a dataset of publicly available audio recordings in synthesized scenarios.

Main Results:

Achieved a true positive rate of 76% for cough event detection.
Recorded an average of 2.85 false positives per minute.
Demonstrated successful automatic event detection and summarization without pre-training.

Conclusions:

The developed algorithm effectively detects cough events in audio recordings using ISA and time-frequency analysis.
This automated approach significantly reduces manual counting time for ambulatory cough monitoring.
The model's performance shows promise for real-world applications in respiratory health assessment.