NDLI: Discrete Mutative Particle Swarm Optimisation of MFCC computation for classifying hypothyroidal infant cry

Content Provider	IEEE Xplore Digital Library
Author	Zabidi, A. Mansor, W. Lee Yoot Khuan Yassin, I.M. Sahak, R.
Copyright Year	2010
Description	Author affiliation: Faculty of Electrical Engineering, University Technology Mara, 40450 Shah Alam, Selangor, Malaysia (Zabidi, A.; Mansor, W.; Lee Yoot Khuan; Yassin, I.M.; Sahak, R.)
Abstract	This paper describes the optimization of Mel Frequency Cepstral Coefficients (MFCC) parameters using Discrete Mutative Particle Swarm Optimization (DMPSO) for diagnosis of hypothyroidism in infants. The MFCC was used to extract the feature set from infant cry signals. The features were then classified using Multi-Layer Perceptron (MLP). The DMPSO variants optimize the number of filter banks and number of cepstral coefficients in MFCC. Based on the values chosen by DMPSO, the extracted features were then fed to 50 MLP classifiers (with different initial weight initialization values), which were trained to discriminate between healthy and hypothyroid infants. The results showed that DMPSO managed to produce classification accuracy of 88.7% with percentage convergence of 66.7% in detecting hypothyroidism from infant cry signals. The optimal number of filter bank and MFC coefficients were found to be 36 and 19 respectively.
Starting Page	588
Ending Page	592
File Size	259050
Page Count	5
File Format	PDF
ISBN	9781424490547
e-ISBN	9781424490554
DOI	10.1109/ICCAIE.2010.5735149
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-05
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pediatrics Optimization methods Feature extraction Biomédical signal processing Classification algorithms Feedforward neural networks Mel frequency cepstral coefficient Particle swarm optimization Artificial intelligence Optimization Particle Swarm Optimization Convergence
Content Type	Text
Resource Type	Article

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