Diagnosis of vocal fold pathology using time-domain features and systole activated neural network
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Institute of Electrical and Elctronics Engineering (IEEE)
2010
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my.unimap-88222010-08-18T06:47:18Z Diagnosis of vocal fold pathology using time-domain features and systole activated neural network Paulraj, Murugesa Pandiyan, Prof. Madya Sazali, Yaacob, Prof. Dr. Hariharan, M. Artificial neural network Systole activation function Time-domain features Voice disorders International Colloquium on Signal Processing and Its Applications (CSPA) Link to publisher's homepage at http://ieeexplore.ieee.org/ Due to the nature of job, unhealthy social habits and voice abuse, the people are subjected to the risk of voice problems. It is well known that most of vocal fold pathologies cause changes in the acoustic voice signal. Therefore, the voice signal can be a useful tool to diagnose them. Acoustic voice analysis can be used to characterize the pathological voices. This paper presents the detection of vocal fold pathology with the aid of the speech signal recorded from the patients. Time-domain features are proposed and extracted to detect the vocal fold pathology. The main advantages of this method are less computation time, possibility of real-time system development and it requires no transformation techniques (frequency transformation or time-frequency transformation). In order to test the effectiveness and reliability of the proposed time-domain features, a simple neural network model with systole activation function is proposed and trained by conventional back propagation (BP) algorithm. The classification accuracy of the proposed systole activated neural network is comparable with the results of neural network model with sigmoidal activation function. The simulation results show that the proposed systole activated neural network reduces the time taken for training the neural network. 2010-08-18T06:47:18Z 2010-08-18T06:47:18Z 2009-03-06 Working Paper p.29-32 978-1-4244-4150-1 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5069181 http://hdl.handle.net/123456789/8822 en Proceedings of the 5th International Colloquium on Signal Processing and Its Applications (CSPA) 2009 Institute of Electrical and Elctronics Engineering (IEEE) |
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Universiti Malaysia Perlis |
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| language |
English |
| topic |
Artificial neural network Systole activation function Time-domain features Voice disorders International Colloquium on Signal Processing and Its Applications (CSPA) |
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Artificial neural network Systole activation function Time-domain features Voice disorders International Colloquium on Signal Processing and Its Applications (CSPA) Paulraj, Murugesa Pandiyan, Prof. Madya Sazali, Yaacob, Prof. Dr. Hariharan, M. Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| description |
Link to publisher's homepage at http://ieeexplore.ieee.org/ |
| format |
Working Paper |
| author |
Paulraj, Murugesa Pandiyan, Prof. Madya Sazali, Yaacob, Prof. Dr. Hariharan, M. |
| author_facet |
Paulraj, Murugesa Pandiyan, Prof. Madya Sazali, Yaacob, Prof. Dr. Hariharan, M. |
| author_sort |
Paulraj, Murugesa Pandiyan, Prof. Madya |
| title |
Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| title_short |
Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| title_full |
Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| title_fullStr |
Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| title_full_unstemmed |
Diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| title_sort |
diagnosis of vocal fold pathology using time-domain features and systole activated neural network |
| publisher |
Institute of Electrical and Elctronics Engineering (IEEE) |
| publishDate |
2010 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/8822 |
| _version_ |
1643789252751785984 |
| score |
13.214268 |