| DOI | Trouver le DOI : https://doi.org/10.1109/BIBE60311.2023.00030 |
|---|
| Auteur | Rechercher : Huq, Saiful; Rechercher : Xi, Pengcheng1; Rechercher : Goubran, Rafik; Rechercher : Knoefel, Frank; Rechercher : Green, James R |
|---|
| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
|
|---|
| Bailleur de fonds | Rechercher : University of Ottawa |
|---|
| Format | Texte, Article |
|---|
| Conférence | 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE), December 4-6, 2023, Dayton, OH, USA |
|---|
| Sujet | cough classification; machine learning; neural networks; biomedical signal processing; data augmentation; mel spectogram, deep learning; audio cough classification, audio; training; deep learning; speech recognition; data models; reverberation; task analysis |
|---|
| Résumé | The global COVID-19 pandemic increased the interest in automatic analysis and classification of cough sounds. However, developing such a system requires a large dataset of expert-labelled cough sounds, which remains elusive. Data augmentation techniques are often employed to train machine learning models with limited data, while ensuring model robustness to real-world variations. We have recently proposed the use of “natural” spectral data augmentation methods, including noise and reverberation [1]. In this paper, we further investigate the method by studying the alignment between training and testing environments. We augment training sound data with varying levels of reverberation and Gaussian noise, and evaluate augmented convolutional neural network models across a range of test environments on two audio classification tasks: speech command recognition and cough sound classification. Results demonstrate the broad robustness of the data-augmented deep learning model across various test environments. Furthermore, the augmented model's performance on cough classification is compared to 4 expert annotations on a large cough dataset and is seen to be near-human-level accuracy. This augmented model is recommended for classifying cough sounds in natural settings beyond laboratory conditions. |
|---|
| Date de publication | 2023-12-04 |
|---|
| Maison d’édition | IEEE |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 7408c380-a3a3-4961-9c4a-99d69611aa9d |
|---|
| Enregistrement créé | 2024-04-04 |
|---|
| Enregistrement modifié | 2024-04-04 |
|---|
