Cough detection by ensembling multiple frequency subband features

Cough is a common symptom in respiratory diseases. Objectively evaluating the quantity and intensity of cough by pattern recognition technologies can provide valuable clinical information for cough diagnosis and monitoring. Cough detection is the basis of cough diagnosis and analysis. It aims at detecting cough events and their exact boundaries from an audio stream. From signal characteristics, it is found that energy distribution scatters in the cough spectrum, which is obviously different from speech signals. However, almost all feature extraction methods for cough detection in previous works are derived from the speech recognition domain. In this article, subband features are obtained by using gammatone filterbank and an audio feature extraction method. Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF) are trained with the corresponding subband features and ensemble method combines the outputs to make the final decision. Experiments are conducted on both synthetic data and real data. The real data is collected from 18 patients with respiratory diseases in clinical environments and annotated by human experts. Experiment results demonstrate that ensembling multiple frequency subbands helps to impove performance in cough detection. Compared with other methods, our method can improve the accuracy by 3.2%.