Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

被引:31
|
作者
Ashwini, K. [1 ]
Vincent, P. M. Durai Raj [1 ]
Srinivasan, Kathiravan [1 ]
Chang, Chuan-Yu [2 ]
机构
[1] Vellore Inst Technol VIT, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India
[2] Natl Yunlin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Touliu, Yunlin, Taiwan
关键词
convolutional neural network; infant cry classification; short time fourier transform; support vector machine; spectrogram; NEURAL-NETWORKS; SYSTEM;
D O I
10.3389/fpubh.2021.670352
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Tool Wear Classification Based on Support Vector Machine and Deep Learning Models
    Hung, Yung-Hsiang
    Huang, Mei-Ling
    Wang, Wen-Pai
    Hsieh, Hsiao-Dan
    SENSORS AND MATERIALS, 2024, 36 (11) : 4815 - 4833
  • [2] When Ensemble Learning Meets Deep Learning: a New Deep Support Vector Machine for Classification
    Qi, Zhiquan
    Wang, Bo
    Tian, Yingjie
    Zhang, Peng
    KNOWLEDGE-BASED SYSTEMS, 2016, 107 : 54 - 60
  • [3] Quantum machine learning for support vector machine classification
    Kavitha, S. S.
    Kaulgud, Narasimha
    EVOLUTIONARY INTELLIGENCE, 2024, 17 (02) : 819 - 828
  • [4] Quantum machine learning for support vector machine classification
    S. S. Kavitha
    Narasimha Kaulgud
    Evolutionary Intelligence, 2024, 17 : 819 - 828
  • [5] Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine
    Wang, Ke-Fan
    An, Jing
    Wei, Zhen
    Cui, Can
    Ma, Xiang-Hua
    Ma, Chao
    Bao, Han-Qiu
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2022, 9
  • [6] Image Classification via Support Vector Machine
    Sun, Xiaowu
    Liu, Lizhen
    Wang, Hanshi
    Song, Wei
    Lu, Jingli
    PROCEEDINGS OF 2015 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2015), 2015, : 485 - 489
  • [7] Deep support vector machine for hyperspectral image classification
    Okwuashi, Onuwa
    Ndehedehe, Christopher E.
    PATTERN RECOGNITION, 2020, 103
  • [8] Deep support vector machine for PoISAR image classification
    Okwuashi, Onuwa
    Ndehedehe, Christopher E.
    Olayinka, Dupe Nihinlola
    Eyoh, Aniekan
    Attai, Hosanna
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (17) : 6502 - 6540
  • [9] Elastic impedance based facies classification using support vector machine and deep learning
    Nishitsuji, Yohei
    Exley, Russell
    GEOPHYSICAL PROSPECTING, 2019, 67 (04) : 1040 - 1054
  • [10] Red Blood Cell Classification: Deep Learning Architecture versus Support Vector Machine
    Aliyu, Hajara Abdulkarim
    Sudirman, Rubita
    Razak, Mohd Azhar Abdul
    Abd Wahab, Muhamad Amin
    2018 2ND INTERNATIONAL CONFERENCE ON BIOSIGNAL ANALYSIS, PROCESSING AND SYSTEMS (ICBAPS 2018), 2018, : 142 - 147