Respiratory Sound Classification Using Long-Short Term Memory
Audio and Speech Processing
2020-08-10 v1 Machine Learning
Sound
Abstract
Developing a reliable sound detection and recognition system offers many benefits and has many useful applications in different industries. This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. Some methods which have been employed such as independent component analysis and blind source separation are examined. Finally, an examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented.
Keywords
Cite
@article{arxiv.2008.02900,
title = {Respiratory Sound Classification Using Long-Short Term Memory},
author = {Chelsea Villanueva and Joshua Vincent and Alexander Slowinski and Mohammad-Parsa Hosseini},
journal= {arXiv preprint arXiv:2008.02900},
year = {2020}
}