# A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features

arXiv: https://arxiv.org/abs/2412.01996 · HTML: https://mlsanity.com/paper/2412.01996/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025)
- [Automatic Cough Analysis for Non-Small Cell Lung Cancer Detection](https://mlsanity.com/paper/2507.19174/) (2025)
- [A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease](https://mlsanity.com/paper/2508.16237/) (2025)
- [Cough activity detection for automatic tuberculosis screening](https://mlsanity.com/paper/2603.11241/) (2026)

## Abstract (arXiv)

Cough is a protective reflex conveying information on the state of the respiratory system. Cough assessment has been limited so far to subjective measurement tools or uncomfortable (i.e., non-wearable) cough monitors. This limits the potential of real-time cough monitoring to improve respiratory care. Objective: This paper presents a machine hearing system for audio-based robust cough segmentation that can be easily deployed in mobile scenarios. Methods: Cough detection is performed in two steps. First, a short-term spectral feature set is separately computed in five predefined frequency bands: [0, 0.5), [0.5, 1), [1, 1.5), [1.5, 2), and [2, 5.5125] kHz. Feature selection and combination are then applied to make the short-term feature set robust enough in different noisy scenarios. Second, high-level data representation is achieved by computing the mean and standard deviation of short-term descriptors in 300 ms long-term frames. Finally, cough detection is carried out using a support vector machine trained with data from different noisy scenarios. The system is evaluated using a patient signal database which emulates three real-life scenarios in terms of noise content. Results: The system achieves 92.71% sensitivity, 88.58% specificity, and 90.69% Area Under Receiver Operating Characteristic (ROC) curve (AUC), outperforming state-of-the-art methods. Conclusion: Our research outcome paves the way to create a device for cough monitoring in real-life situations. Significance: Our proposal is aligned with a more comfortable and less disruptive patient monitoring, with benefits for patients (allows self-monitoring of cough symptoms), practitioners (e.g., assessment of treatments or better clinical understanding of cough patterns), and national health systems (by reducing hospitalizations).