Title :
An SVM-based classification of oral and nasal snoring sounds with Kullback-Leibler kernel
Author :
Mikami, Tsuyoshi ; Kojima, Yohichiro ; Yonezawa, Kazuya ; Yamamoto, Masahito ; Furukawa, Masashi
Author_Institution :
Tomakomai Coll. of Technol., Tomakomai, Japan
Abstract :
Recently, numerous investigations have shown that loud habitual snoring is due to nasal obstruction and loud oral snoring is found in many sleep apnea patients. So, it is important to detect oral snoring in the earlier stage, but unfortunately we cannot know our own sleep condition. For such purpose, we adopt a Support Vector Machine (SVM) classifier with Kullback-Leibler (KL) kernel so as to classify oral and nasal snoring sounds based on the spectral properties, and compare it with the other kernel functions.
Keywords :
acoustic signal detection; medical disorders; pattern classification; signal classification; sleep; spectral analysis; support vector machines; KL kernel; Kullback-Leibler kernel; SVM classifier; SVM-based classification; kernel functions; loud habitual snoring; loud oral snoring; nasal obstruction; nasal snoring sound classification; nasal snoring sounds; oral snoring detection; oral snoring sound classification; oral snoring sounds; sleep apnea patients; sleep condition; spectral property; support vector machine classifier; Accuracy; Acoustics; Educational institutions; Kernel; Mouth; Sleep apnea; Support vector machines; Kullback-Leibler divergence; pattern classification; snoring sounds; support vector machines;
Conference_Titel :
SICE Annual Conference (SICE), 2012 Proceedings of
Conference_Location :
Akita
Print_ISBN :
978-1-4673-2259-1
