  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.
