Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds
Machine Learning
2020-12-29 v1 Computer Vision and Pattern Recognition
Sound
Audio and Speech Processing
Abstract
This paper proposes a robust deep learning framework used for classifying anomaly of respiratory cycles. Initially, our framework starts with front-end feature extraction step. This step aims to transform the respiratory input sound into a two-dimensional spectrogram where both spectral and temporal features are well presented. Next, an ensemble of C- DNN and Autoencoder networks is then applied to classify into four categories of respiratory anomaly cycles. In this work, we conducted experiments over 2017 Internal Conference on Biomedical Health Informatics (ICBHI) benchmark dataset. As a result, we achieve competitive performances with ICBHI average score of 0.49, ICBHI harmonic score of 0.42.
Cite
@article{arxiv.2012.13668,
title = {Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds},
author = {Dat Ngo and Lam Pham and Anh Nguyen and Ben Phan and Khoa Tran and Truong Nguyen},
journal= {arXiv preprint arXiv:2012.13668},
year = {2020}
}
Comments
5 pages, 2 figures, 8 tables