Limited receptive area neural classifier for recognition of swallowing sounds using continuous wavelet transform.
In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and continuous wavelet transform (CWT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where scalograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.
Makeyev
Oleksandr
O
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699 USA. mckehev@cias.clarkson.edu
Sazonov
Edward
E
Schuckers
Stephanie
S
Lopez-Meyer
Paulo
P
Melanson
Ed
E
Neuman
Michael
M
D000465:Algorithms; D001314:Auscultation / Q000379:methods*; D003679:Deglutition / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*