Novel feature extraction method for cough detection using NMF

Cough is a common symptom in respiratory diseases. To provide valuable clinical information for cough diagnosis and monitoring, objectively evaluating the quantity and intensity of cough based on cough detection by pattern recognition technologies is needed. Cough detection aims to extract the boundaries of cough events from an audio stream. From spectral visualisation, it is found that the energy spectrum of cough signal spreads widely in the whole frequency band, which is very different from a speech signal. However, almost all feature extraction methods for cough detection in the previous work are derived from speech recognition region. In this study, to find the difference of cough and other audios in a more compact representation, non-negative matrix factorisation (NMF) is exploited to extract the spectral structure from signals. Furthermore, the spectral structure from cough signal can be used as filter banks of feature extraction methods, which makes the filter banks more suitable for cough detection than manually designed ones. Besides, parameterisation for the spectral structure also provides an optimising strategy for the authors’ NMF-based feature extraction method. Experiments are conducted on real data. The results demonstrate that NMF-based feature extraction method has considerable potential in improving performance for cough detection.