Abstract / truncated to 115 words (read the full abstract)

Auscultation with a stethoscope enables us to recognize pathological changes of the lung. It is a fast and inexpensive diagnosis method. However, it has several disadvantages: subjectiveness, i.e. the lung sound evaluation depends on the experience of physicians, can not provide continuous monitoring and a trained expert is required. Furthermore, the characteristics of the lung sounds are in the low frequency range, where the human hearing has limited sensitivity and is susceptible to noise artifacts. Exploiting the advances in digital recording devices, signal processing and machine learning, computational methods for the analysis of lung sounds have been a successful and effective approach. Computational lung sound analysis is beneficial for computer-supported diagnosis, digital storage and monitoring ... toggle 3 keywords

lung sound classification – acoustic scene classification – deep learning

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