Respiratory Sound Classification Using Long-Short Term Memory

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 method...

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Bibliographic Details
Published inarXiv.org
Main Authors Villanueva, Chelsea, Vincent, Joshua, Slowinski, Alexander, Mohammad-Parsa Hosseini
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.08.2020
Subjects
Classification
Independent component analysis
Machine learning
Respiratory diseases
Short term
Signal processing
Sound
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Summary: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.
ISSN:2331-8422