Title: Time-frequency analysis-based method for application of infant cry classification

Authors: J. Saraswathy; M. Hariharan; Wan Khairunizam; J. Sarojini; Sazali Yaacob

Addresses: School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia ' Department of Electronics Engineering, National Institute of Technology, Srinagar (Garhwal)-246174, Uttarakhand, India ' School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Perlis, Malaysia ' School of Bioprocess Engineering, University Malaysia Perlis (UniMAP), Campus Jejawi 3, 02600 Arau, Perlis, Malaysia ' Universiti Kuala Lumpur Malaysian Spanish Institute, Kulim Hi-Tech Park, 09000 Kulim, Kedah, Malaysia

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 newborn 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.

Keywords: infant cry; medical engineering; signal processing; time-frequency analysis; wavelet packet spectrum; feature extraction; t-f features; mel-frequency cepstral coefficients; MFCCs; linear prediction coefficients; LPCs; classification; probabilistic neural network; PNN; general regression neural network; GRNN.

DOI: 10.1504/IJMEI.2020.106897

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.2, pp.119 - 134

Received: 04 Aug 2017
Accepted: 07 May 2018

Published online: 27 Apr 2020 *

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