Paper Info
Title
Snore Sound Recognition: On Wavelets And Classifiers From Deep Nets To Kernels
Abstract
In this paper, we present a comprehensive comparison of wavelet features for the classification of snore sounds. Wavelet features have proven to be efficient in our previous work; however, the benefits of wavelet transform energy (WTE) and wavelet packet transform energy (WPTE) features were not clearly established. In this study, we firstly present our updated snore sounds database, expanded from 24 patients (collected by one medical centre) to 40 patients (collected by three medical centres). We then study the effects of varying frame sizes and overlaps for extraction of the wavelet low-level descriptors, the effect of which have yet to be fully established. We also compare the performance of the WTE and WPTE features when fed into multiple classifiers, namely, Support Vector Machines (SVM), K-Nearest Neighbours, Linear Discriminant Analysis, Random Forests, Extreme Learning Machines, Kernel Extreme Learning Machines, Multilayer Perceptron, and Deep Neural Networks. Key results presented indicate that, when fed into a SVM, WTE outperforms WPTE (one-tailed z-test, p < 0.002). Further, WPTE can achieve a significant improvement when trained by a k-nearest neighbours classifier (one-tailed z-test, p < 0.001).
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037669
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Pattern recognition,Computer science,Support vector machine,Speech recognition,Multilayer perceptron,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Random forest,Wavelet packet decomposition,Wavelet,Wavelet transform
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
3
8
Name
Order
Citations
PageRank
Kun Qian14017.69
Christoph Janott221.85
Jun Deng327818.59
Clemens Heiser4172.51
Winfried Hohenhorst5171.49
Michael Herzog6171.83
Nicholas Cummins734932.93
Björn Schuller86749463.50