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Time-frequency analysis based methods for classification of newborn cry signals

Doctor of Philosophy in Medical Electronic Engineering

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Bibliographic Details
Main Author: Saraswathy, Jeyaraman
Other Authors: Hariharan, Muthusamy, Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2016
Subjects:
Crying in infants
Signal processing
Neural networks (Computer science)
Wavelets (Mathematics)
Wigner distribution
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77899
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spelling my.unimap-778992023-02-21T07:03:17Z Time-frequency analysis based methods for classification of newborn cry signals Saraswathy, Jeyaraman Hariharan, Muthusamy, Dr. Crying in infants Signal processing Neural networks (Computer science) Wavelets (Mathematics) Wigner distribution Doctor of Philosophy in Medical Electronic Engineering The infant cry classification implies non invasive objective methods, classification of different patterns of infant cry utterances and adoption of artificial and digital signal processing techniques. It has been commenced past decades ago to overcome the limitations of subjective methods in particularly auditory perception and human spectrographic analysis, which are relying on clinical rater‘s experience and expertise. This thesis addresses the development of an objective method for classification of newborn cries primarily using time-frequency (t-f) methods. Towards this aim, a novel investigation using two different t-f based signal processing approaches was performed: (a) Quadratic time-frequency distributions (QTFDs): Spectrogram (SPEC), Wigner- Ville distribution (WVD), Smoothed-Wigner Ville distribution (SWVD), Choi-William distribution (CWD) and Modified B-distribution (MBD), and (b) Wavelet packet transform (WPT) based method: wavelet packet spectrum (Wpspectrum). The effectiveness of the suggested t-f methods was analyzed using normal and different pathological cry signals. The investigational cry signals were accessed from three different origins of databases (Mexico, Hungary and Malaysia (self-developed database). In order to investigate the effectiveness of the suggested t-f methods, eight different cry experiments were suggested, including binary and multiclass problems. In the binary domain, analysis of cry signals from different origin and the severity level of pathological cry signals were considered for investigation. The framework of this work was designed in two phases in order to compare the performance evaluation of the suggested t-f methods with the state of the art attributes in the infant cry classification area (Mel frequency cepstral coefficients (MFCCs) and Linear prediction coefficients (LPCs)). Initially, the performance evaluation of the individual suggested t-f methods, MFCCs and LPCs on different proposed cry datasets were performed. In this case, a cluster of t-f based statistical features was extracted from the suggested t-f methods. The performance evaluation in term of classification task was tackled using two different supervised neural networks, namely Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN). Subsequently, by considering the classification performance, the best distribution from the QTFDs was selected. In the second phase, a feature set, combination of MFCCs, LPCs and the extracted statistical features from the best QTFDs and Wpspectrum was formed. Different feature selection techniques, such as Plus-1-minus-r (LRS) and Information Gain (IGS) were applied on the formed feature set to obtain a parsimonious subset of those features. The discrimination capability of the selected feature vector in terms of classification accuracy was evaluated using PNN and GRNN. 2016 2023-02-21T07:00:28Z 2023-02-21T07:00:28Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77899 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Crying in infants
Signal processing
Neural networks (Computer science)
Wavelets (Mathematics)
Wigner distribution
spellingShingle Crying in infants
Signal processing
Neural networks (Computer science)
Wavelets (Mathematics)
Wigner distribution
Saraswathy, Jeyaraman
Time-frequency analysis based methods for classification of newborn cry signals
description Doctor of Philosophy in Medical Electronic Engineering
author2 Hariharan, Muthusamy, Dr.
author_facet Hariharan, Muthusamy, Dr.
Saraswathy, Jeyaraman
format Thesis
author Saraswathy, Jeyaraman
author_sort Saraswathy, Jeyaraman
title Time-frequency analysis based methods for classification of newborn cry signals
title_short Time-frequency analysis based methods for classification of newborn cry signals
title_full Time-frequency analysis based methods for classification of newborn cry signals
title_fullStr Time-frequency analysis based methods for classification of newborn cry signals
title_full_unstemmed Time-frequency analysis based methods for classification of newborn cry signals
title_sort time-frequency analysis based methods for classification of newborn cry signals
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2016
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77899
_version_ 1772813083436122112
score 13.222552

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