Benutzer: Gast  Login
Weniger Felder
Einfache Suche
Titel:

Snore sound recognition: On wavelets and classifiers from deep nets to kernels.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Kun Qian; Janott, Christoph; Jun Deng; Heiser, Clemens; Hohenhorst, Winfried; Herzog, Michael; Cummins, Nicholas; Schuller, Bjorn
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).
Zeitschriftentitel:
Conf Proc IEEE Eng Med Biol Soc
Jahr:
2017
Band / Volume:
2017
Seitenangaben Beitrag:
3737-3740
Sprache:
eng
Volltext / DOI:
doi:10.1109/EMBC.2017.8037669
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/29060710
Print-ISSN:
1557-170X
TUM Einrichtung:
Hals-Nasen-Ohrenklinik und Poliklinik
 BibTeX