Browsing Hariharan Muthusamy, Dr. by Subject "Feature extraction"
Now showing items 1-7 of 7
-
Application of feedforward neural network for the classification of pathological voices
(Universiti Teknologi MARA (UiTM), 2007-03-09)This paper present the application of feed forward neural network for the classification of pathological voices based on the on the acoustic analysis and EGG features. Acoustic analysis is a non-invasive technique based ... -
Detection of vocal fold paralysis and edema using time-domain features and probabilistic neural network
(Inderscience Publisher, 2011)This paper proposes a feature extraction method based on time-domain energy variation for the detection of vocal fold pathology. In this work, two different vocal fold problems (vocal fold paralysis and edema) are taken ... -
Feature extraction based on mel-scaled wavelet packet transform for the diagnosis of voice disorders
(SpringerLink, 2008-06-25)Feature extraction from the vocal signal plays very important role in the area of automatic detection of voice disorders. Many feature extraction algorithms have been developed in the last three decades based on acoustic ... -
Feature extraction for biometric recognition with photoplethysmography signals
(IEEE Conference Publications, 2013-04)Photoplethysmography (PPG) signals stand out due to features such as readily accessible, high reliability and confidentiality, the ease of use etc. among bio-signals. The feasibility studies carried out on the PPG signals ... -
A hybrid expert system approach for telemonitoring of vocal fold pathology
(Elsevier B.V., 2013)Acoustical parameters extracted from the recorded voice samples are actively pursued for accurate detection of vocal fold pathology. Most of the system for detection of vocal fold pathology uses high quality voice samples. ... -
Infant cry classification to identify asphyxia using time-frequency analysis and radial basis neural networks
(Elsevier Ltd, 2012-08)A cry is the first verbal communication of infants and it is described as a loud, high-pitched sound made by infants in response to certain situations. Infant cry signals can be used to identify physical or psychological ... -
Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network
(Elsevier Ireland Ltd., 2012-11)Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and ...