Evaluating classification methods in snore activity detection

Abstract

Health improvement and maintenance is becoming increasingly important and depends on three elements: nutrition, exercise, and rest (sleep). In the present work, we focus on sleep and develop a smartphone-based system based on snore activity detection to investigate day-to-day variations in the sleep states, which requires no dedicated hardware. Snore activity detection is performed using classification methods to detect the snore activity using acoustic features. As acoustic features, the sound pressure level and mel-frequency cepstrum coefficients are calculated from the sleep sound data obtained using a smartphone. In this study, we evaluated the performance of three classification methods, support vector machine, multi-kernel learning using support vector machine and deep learning in snore activity detection.