A Study on the Optimum Number of Training Data in Snore Activity Detection Using SVM

Abstract

Health promotion and maintenance is becoming increasingly important and depends on three elements: nutrition, exercise, and rest (sleep). In the present study, focusing on sleep, we develop a smartphone-based system based on snore activity detection to investigate day-to-day variations in the sleep state, which does not require dedicated hardware. Here, we analyze the number of training data required for snore activity detection using a support vector machine (SVM), and we consider ways to improve detection performance. The sound pressure level and mel-frequency cepstrum coefficients are calculated from sleep sound data obtained using a smartphone. Snore activity detection is performed by machine learning using an SVM with a linear kernel, the SVM is trained by labeled acoustic features, and the trained SVM models are used to detect snore activity. In general, the accuracy of the generated models increases with the increasing number of training data in the learning algorithm, which in turn increases the computational cost, therefore, a balance between accuracy and cost efficiency is much required. We investigate the relation between the detection rate and the number of training data in snore activity detection, and we propose the optimum number of data required for learning.