Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves
Loading...
Files
Published version
Date
2024-09-03
Authors
Van Truong, Hoang
Tran, Khanh-Tung
Vu, Xuan-Son
Nguyen, Duy-Khuong
Bhuyan, Monowar
Nguyen, Hoang D.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Published Version
Abstract
This paper investigates a novel graph-based representation of sound waves inspired by the physical phenomenon of correlated vibrations. We propose a Wave2Graph framework for integrating multiple acoustic representations, including the spectrum of frequencies and correlations, into various neural computing architectures to achieve new state-of-the-art performances in sound classification. The capability and reliability of our end-to-end framework are evidently demonstrated in voice pathology for low-cost and non-invasive mass-screening of medical conditions, including respiratory illnesses and Alzheimer’s Dementia. We conduct extensive experiments on multiple public benchmark datasets (ICBHI and ADReSSo) and our real-world dataset (IJSound: Respiratory disease detection using coughs and breaths). Wave2Graph framework consistently outperforms previous state-of-the-art methods with a large magnitude, up to 7.65% improvement, promising the usefulness of graph-based representation in signal processing and machine learning.
Description
Keywords
Wave2Graph , Graph neural network , Correlation , Sound signal processing , Neural network architecture
Citation
Hoang, V.-T., Tran, K.-T., Vu, X.-S., Nguyen, D.-K., Bhuyan, M. and Nguyen, H.D. (2024) ‘Wave2Graph: Integrating spectral features and correlations for graph-based learning in sound waves’, AI Open, 5, pp. 115–125. Available at: https://doi.org/10.1016/j.aiopen.2024.08.004