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- Title
INFANT CRY RECOGNITION SYSTEM USING AUTOREGRESSIVE MODEL COEFFICIENTS.
- Authors
FATIMAH, S. R.; AIBINU, A. M.
- Abstract
Understanding infants' needs through crying is a skill acquired by health care givers as well as parents from training and experiences. However, errors may evolve due to variations in judgment and limitations on the human sensory system. Various approaches have been proposed to mimic the classical human based method, which also tied results to system dominant errors. This work uses the Autoregressive (AR) model coefficient as features for recognizing infant cry. First, a dataset of infant cry consisting of Hunger, Pain, and Normal cry was obtained. Each cry was framed and widowed with overlap to enable the processing of the rapidly changing cry signal. Then AR model coefficients (features) were extracted from the trained Artificial Neural Network (ANN). The extracted features were then used to train an Artificial Neural Network recognition system. The performance of this system was tested using three different activation functions, sampling frequencies, and various threshold values. Results show the appropriateness of this new approach.
- Subjects
CRYING in infants; AUTOMATIC speech recognition; AUTOREGRESSIVE models; ARTIFICIAL neural networks; PATHOLOGY; PATTERN perception; SIGNAL processing
- Publication
I-Manager's Journal on Digital Signal Processing, 2018, Vol 6, Issue 2, p9
- ISSN
2321-7480
- Publication type
Article
- DOI
10.26634/jdp.6.2.15591