# A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease

arXiv: https://arxiv.org/abs/2508.16237 · HTML: https://mlsanity.com/paper/2508.16237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App](https://mlsanity.com/paper/2004.01275/) (2020)
- [COVID-19 Cough Classification using Machine Learning and Global Smartphone Recordings](https://mlsanity.com/paper/2012.01926/) (2020)
- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System](https://mlsanity.com/paper/1509.02512/) (2015)
- [Objective Study of Sensor Relevance for Automatic Cough Detection](https://mlsanity.com/paper/2001.00537/) (2019)
- [Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19](https://mlsanity.com/paper/2103.01806/) (2021)
- [Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey](https://mlsanity.com/paper/2309.14383/) (2023)
- [A Machine Hearing System for Robust Cough Detection Based on a High-Level Representation of Band-Specific Audio Features](https://mlsanity.com/paper/2412.01996/) (2024)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds](https://mlsanity.com/paper/2106.12174/) (2021)
- [XAI-Driven Spectral Analysis of Cough Sounds for Respiratory Disease Characterization](https://mlsanity.com/paper/2508.14949/) (2025)

## Abstract (arXiv)

This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.