Respiratory Sound Classification Using Long-Short Term Memory
This is an incremental approach to a domain-specific problem in healthcare, focusing on improving respiratory disease classification.
The paper tackled the problem of respiratory sound classification for disease detection by exploring deep learning and long short-term memory networks, but did not report specific results or numbers.
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.