Premature Infant Cry Classification via Deep Convolutional Recurrent Neural Network Based on Multi-class Features
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s00034-023-02457-5.pdf
Reference25 articles.
1. K. Ashwini, D.R.V. PM, K. Srinivasan, C.Y. Chang, Deep convolutional neural network-based feature extraction with optimized machine learning classifier in infant cry classification, in 2020 International Conference on Decision Aid Sciences and Application (IEEE, 2020), pp. 27–32
2. K. Ashwini, P.D.R. Vincent, A deep convolutional neural network-based approach for effective premature infant cry classification. Recent Adv. Comput. Sci. Commun. 15(2), 229–239 (2022)
3. C.Y. Chang, S. Bhattacharya, P.M.R. Vincent, K. Lakshmanna, K. Srinivasan, An efficient classification of neonates cry using extreme gradient boosting-assisted grouped-support-vector network. J. Healthc. Eng. (2021). https://doi.org/10.1155/2021/7517313
4. R. Cohen, D. Ruinskiy, J. Zickfeld, H. IJzerman, Y. Lavner, Baby cry detection: deep learning and classical approaches, in Development and Analysis of Deep Learning Architectures (2020), pp. 171–196
5. S.P. Dewi, A.L. Prasasti, B. Irawan, The study of baby crying analysis using MFCC and LFCC in different classification methods, in 2019 IEEE International Conference on Signals and Systems (ICSigSys) (IEEE, 2019), pp. 18–23
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