Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25170
Title: Time-frequency analysis-based method for application of infant cry classification
Authors: Saraswathy, J.
Hariharan, M.
Khairunizam, W.
Sarojini, J.
Yaacob, S.
UniKL MSI
Keywords: infant cry
signal processing
medical engineering
time-frequency analysis
wavelet packet spectrum
Issue Date: 16-Nov-2021
Abstract: Automatic infant cry classification is one of the significant studies under medical engineering, adopting the medical and engineering techniques for the classification of diverse physical and physiological states of the infants. This paper proposes a new investigation of time-frequency (t-f)-based signal processing technique using wavelet packet spectrum (wpspectrum) for classification of new born cry signals. The study was initialised with the extraction of a cluster of t-f features from the generated t-f matrix of recorded cry signals using wpspectrum by extending time-domain and frequency-domain features to the joint t-f domain. In accordance, conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the performance of the suggested t-f approach. Probabilistic neural network (PNN) and general regression neural network (GRNN) were used in classification. The proposed methodology was implemented to classify different sets of infant cry signals and the best empirical result of above 99% was reported.
URI: http://hdl.handle.net/123456789/25170
Appears in Collections:Conference Papers



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.