Estimation of noise suppression parameters for maximizing snoring activity detection performance

The optimal parameters of noise suppression for detection of snoring activity are analyzed and we improve performance of detection of snoring activity in this paper. For detection of snoring activity, we use a Support Vector Machine which is one of machine learning. By training of grand truth and features, the SVM model is obtained. By applying test date to the SVM model, it is classified into snoring or non-snoring class. Using a mobile device, sleeping sound is recorded. The features for machine learning are computed from sleeping sound. To improve the detection performance, noise suppression is performed before features extraction. We examine the relation between the noise suppression parameters and the performance of detection of snoring activity. We investigate the optimal parameters of noise suppression for detection of snoring activity.