Snore activity detection using smartphone sensors

In this paper, we analyze the effects of ambient noise on snore activity detection, and consider ways to improve detection performance. A smartphone is used to obtain sleep sound data, from which the acoustic features of sound pressure level (SPL) and Mel-frequency cepstrum coefficients (MFCC) are calculated. Snore activity detection is performed by machine learning using a support vector machine (SVM) with a linear kernel. The SVM is trained by the labeled acoustic features, and the trained SVM models are used to detect snore activity. Adding ambient noise recorded before sleep to the training set is expected to improve detection performance. Experimental results showed that an improvement in detection performance from F-measure of 0.75 to 0.81 using SPL, from F-measure of 0.62 to 0.62 using MFCC, from F-measure of 0.69 to 0.74 using SPL and MFCC on average.