Acoustic Analysis and Classification of Infant Cry: A New Approach

T. Shreekanth*, S. Saraswathi**
* Assistant Professor, Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India.
** Lecturer, JSS Institute of Speech and Hearing, Mysuru, India.
Periodicity:April - June'2017


In this work, a computational platform has been provided for a specific case study of infant cry analysis for identifying the abnormal infant cry, which is based on the principle of supervised classification, requiring the design of a proper knowledge base with a prior known normal infant cry samples called control sample. This research has a much focused 2-class problem, to be very precise it is a class and a complimentary class problem, where class refers to a healthy normal infant cry class and a complimentary-class refers to an unhealthy abnormal infant cry class. In this study, the authors have made use of Discrete Wavelet Transform (DWT), Mel Frequency Cepstral Coefficients (MFCC), Vector Quantization (VQ), and Euclidean Distance measure. The nearest match of the test infant cry sample is identified by correlating it with the infant cry samples present in the database and then it is classified as normal or abnormal (pathological) infant cry. The proposed method used 100 normal and 100 abnormal samples for training. The algorithm has been tested on the test dataset consisting of 25 normal and abnormal samples and the efficiency is found to be 96%.


Discrete Wavelet Transform (DWT), Mel Frequency Cepstral Co-efficient (MFCC), Vector Quantization (VQ), Euclidean Distance.

How to Cite this Article?

Shreekanth, T., and Saraswathi, S. (2017). Acoustic Analysis and Classification of Infant Cry: A New Approach. i-manager’s Journal on Digital Signal Processing, 5(2), 1-7.


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