Recognition of Swallowing Sounds Using Time- Frequency Decomposition and Limited Receptive Area Neural Classifier

In this paper we propose a novel swallowing sound recognition technique based on the limited receptive area (LIRA) neural classifier and timefrequency decomposition. Time-frequency decomposition methods commonly used in sound recognition increase dimensionality of the signal and require steps of feature selection and extraction. Quite often feature selection is based on a set of empirically chosen statistics, making the pattern recognition dependent on the intuition and skills of the investigator. A limited set of extracted features is then presented to a classifier. The proposed method avoids the steps of feature selection and extraction by delegating them to a limited receptive area neural (LIRA) classifier. LIRA neural classifier utilizes the increase in dimensionality of the signal to create a large number of random features in the time-frequency domain that assure a good description of the signal without prior assumptions of the signal properties. Features that do not provide useful information for separation of classes do not obtain significant weights during classifier training. The proposed methodology was tested on the task of recognition of swallowing sounds with two different algorithms of time-frequency decomposition, short-time Fourier 1 Department of Electrical and Computer Engineering, Clarkson University, Potsdam NY USA, [email protected] 2 Department of Electrical and Computer Engineering, Clarkson University, Potsdam NY USA, [email protected] 3 Department of Electrical and Computer Engineering, Clarkson University, Potsdam NY USA, [email protected] 4 Department of Electrical and Computer Engineering, Clarkson University, Potsdam NY USA, [email protected] 5 Center of Applied Research and Technological Development, National Autonomous University of Mexico, Mexico City Mexico, [email protected] 6 Center for Human Nutrition, University of Colorado Health Sciences Center, Denver CO USA, [email protected] 7 Department of Biomedical Engineering, Michigan Technological University, Houghton MI USA, [email protected] O. Makeyev, E. Sazonov, S. Schuckers, P. Lopez-Meyer, T. Baidyk, E. Melanson and M. Neuman transform (STFT) and continuous wavelet transform (CWT). The experimental results suggest high efficiency and reliability of the proposed approach.