Ting, Hua-Nong and Choo, Yao-Mun and Kamar, Azanna Ahmad (2022) Classification of asphyxia infant cry using hybrid speech features and deep learning models. Expert Systems with Applications, 208. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2022.118064.
Full text not available from this repository.Abstract
Single speech feature such as Mel-Frequency Cepstral Coefficient (MFCC) has been used in most of the studies to classify asphyxia cry among infants. Other speech features such as Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz have not been reported in any study related to the classification of asphyxia cry. The study investigated the use of hybrid features of MFCC, Chromagram, Mel-scaled Spectrogram, Spectral Contrast and Tonnetz and deep learning models in classifying asphyxia cry. Deep learning models such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN) were used to classify infant cry between normal/non-asphyxia and asphyxia. The performance of the deep learning models was compared using concatenated hybrid features and single feature of MFCC. The Baby Chillanto Database was used in this study. CNN model performed better than DNN models when MFCC was used. DNN models performed better with hybrid features compared to that with single feature of MFCC. DNN with multiple hidden layers achieved an accuracy of 100% in classifying normal and asphyxia cry, and 99.96% for non-asphyxia and asphyxia cry when the hybrid features were used.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya [GPF074A-2018] |
| Uncontrolled Keywords: | Asphyxia; Infant cry; Hybrid features; Deep Neural Network; Convolutional Neural Network |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Aug 2023 03:00 |
| Last Modified: | 28 Aug 2023 03:00 |
| URI: | http://eprints.um.edu.my/id/eprint/40947 |
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