Skip to main content
Erschienen in:

04.07.2024

Premature Infant Cry Classification via Elephant Herding Optimized Convolutional Gated Recurrent Neural Network

verfasst von: V. Vaishnavi, M. Braveen, N. Muthukumaran, P. Poonkodi

Erschienen in: Circuits, Systems, and Signal Processing | Ausgabe 10/2024

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Premature babies scream to make contact with their mothers or other people. Infants communicate via their screams in different ways based on the motivation behind their cries. A considerable amount of work and focus is required these days to preprocess, extract features, and classify audio signals. This research aims to propose a novel Elephant Herding Optimized Deep Convolutional Gated Recurrent Neural Network (EHO-DCGR net) for classifying cry signals from premature babies. Cry signals are first preprocessed to remove distortion caused by short sample times. MFCC (Mel-frequency cepstral coefficient), Power Normalized Cepstral Coefficients (PNCC), BFCC (Bark-frequency cepstral coefficient), and LPCC (Linear Prediction cepstral coefficient) are used to identify abnormal weeping through their prosodic aspects. The Elephant Herding optimization (EHO) algorithm is utilized for choosing the best features from the extracted set to form a fused feature matrix. These characteristics are then used to categorize premature baby cry sounds using the DCGR net. The proposed EHO-DCGR net effectiveness is measured by precision, specificity, recall, and F1-score, accuracy. According to experimental fallouts, the proposed EHO-DCGR net detects baby cry signals with an astounding 98.45% classification accuracy. From the experimental analysis, the EHO-DCGR Net increases the overall accuracy by 12.64%, 3.18%, 9.71% and 3.50% better than MFCC-SVM, DFFNN, SVM-RBF and SGDM respectively.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik. 

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information. 

Order your 30-days-trial for free and without any commitment.

Weitere Produktempfehlungen anzeigen
Literatur
1.
Zurück zum Zitat A. Abbaskhah, H. Sedighi, H. Marvi, Infant cry classification by MFCC feature extraction with MLP and CNN structures. Biomed. Signal Process. Control 86, 105261 (2023)CrossRef A. Abbaskhah, H. Sedighi, H. Marvi, Infant cry classification by MFCC feature extraction with MLP and CNN structures. Biomed. Signal Process. Control 86, 105261 (2023)CrossRef
2.
Zurück zum Zitat A. Agasthian, P. Rajendra, L.A. Kumaraswamidhas, Integration of monitoring and security based deep learning network for wind turbine system. Int. J. Syst. Design Comput. 01(01), 11–17 (2023) A. Agasthian, P. Rajendra, L.A. Kumaraswamidhas, Integration of monitoring and security based deep learning network for wind turbine system. Int. J. Syst. Design Comput. 01(01), 11–17 (2023)
3.
Zurück zum Zitat K. Anusha, B. Muthu Kumar, J. Ragaventhiran, India-Net: IOT intrusion detection via enhanced transient search optimized advanced deep learning technique. Int. J. Data Sci. Artif. Intell. IJDSAI 02(01), 07–12 (2024) K. Anusha, B. Muthu Kumar, J. Ragaventhiran, India-Net: IOT intrusion detection via enhanced transient search optimized advanced deep learning technique. Int. J. Data Sci. Artif. Intell. IJDSAI 02(01), 07–12 (2024)
4.
Zurück zum Zitat A. Appathurai, R. Sundarasekar, C. Raja, E.J. Alex, C.A. Palagan, A. Nithya, An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits Syst. Signal Process. 39, 734–756 (2020)CrossRef A. Appathurai, R. Sundarasekar, C. Raja, E.J. Alex, C.A. Palagan, A. Nithya, An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system. Circuits Syst. Signal Process. 39, 734–756 (2020)CrossRef
5.
Zurück zum Zitat K. Ashwini, P.D.R. Vincent, A deep convolutional neural network-based approach for effective neonatal cry classification. Recent Adv. Comput. Sci. Commun. (Formerly: Recent Patents on Computer Science) 15(2), 229–239 (2022)CrossRef K. Ashwini, P.D.R. Vincent, A deep convolutional neural network-based approach for effective neonatal cry classification. Recent Adv. Comput. Sci. Commun. (Formerly: Recent Patents on Computer Science) 15(2), 229–239 (2022)CrossRef
6.
Zurück zum Zitat Bano, S., RaviKumar, K.M.: Decoding baby talk: A novel approach for normal infant cry signal classification. In 2015 International Conference on Soft-Computing and Networks Security (ICSNS), 1–4 (2015). IEEE. Bano, S., RaviKumar, K.M.: Decoding baby talk: A novel approach for normal infant cry signal classification. In 2015 International Conference on Soft-Computing and Networks Security (ICSNS), 1–4 (2015). IEEE.
7.
Zurück zum Zitat P. Banumathi, G.M. Nasira, B. Muthukumar, Artificial neural network technique, statistical and FFT in identifying defect in plain woven fabric. Entropy 8, 8 P. Banumathi, G.M. Nasira, B. Muthukumar, Artificial neural network technique, statistical and FFT in identifying defect in plain woven fabric. Entropy 8, 8
8.
Zurück zum Zitat W. Boulila, A. Alzahem, A. Koubaa, B. Benjdira, A. Ammar, Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Comput. Electron. Agric. 212, 108154 (2023)CrossRef W. Boulila, A. Alzahem, A. Koubaa, B. Benjdira, A. Ammar, Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Comput. Electron. Agric. 212, 108154 (2023)CrossRef
9.
Zurück zum Zitat C.Y. Chang, L.Y. Tsai. in Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019), vol. 3, A CNN-based method for infant cry detection and recognition (2019), 786–792 C.Y. Chang, L.Y. Tsai. in Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019), vol. 3, A CNN-based method for infant cry detection and recognition (2019), 786–792
10.
Zurück zum Zitat S. Chauhan, M. Singh, A.K. Aggarwal, Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy. Meas. 179, 109445 (2021)CrossRef S. Chauhan, M. Singh, A.K. Aggarwal, Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy. Meas. 179, 109445 (2021)CrossRef
11.
Zurück zum Zitat S. Chauhan, M. Singh, A.K. Aggarwal, Cluster head selection in heterogeneous wireless sensor network using a new evolutionary algorithm. Wireless Pers. Commun. 119, 585–616 (2021)CrossRef S. Chauhan, M. Singh, A.K. Aggarwal, Cluster head selection in heterogeneous wireless sensor network using a new evolutionary algorithm. Wireless Pers. Commun. 119, 585–616 (2021)CrossRef
12.
Zurück zum Zitat R. Cohen, D. Ruinskiy, J. Zickfeld, H. IJzerman, Y. Lavner, Baby cry detection: deep learning and classical approaches. Develop. Anal. Deep Learn. Architect. 171–196 (2020). R. Cohen, D. Ruinskiy, J. Zickfeld, H. IJzerman, Y. Lavner, Baby cry detection: deep learning and classical approaches. Develop. Anal. Deep Learn. Architect. 171–196 (2020).
13.
Zurück zum Zitat G. Coro, S. Bardelli, A. Cuttano, R.T. Scaramuzzo, M. Ciantelli, A self-training automatic infant-cry detector. Neural Comput. Appl. 35(11), 8543–8559 (2023)CrossRef G. Coro, S. Bardelli, A. Cuttano, R.T. Scaramuzzo, M. Ciantelli, A self-training automatic infant-cry detector. Neural Comput. Appl. 35(11), 8543–8559 (2023)CrossRef
14.
Zurück zum Zitat S.P. Dewi, A.L. Prasasti, B. Irawan, The study of baby crying analysis using MFCC and LFCC in different classification methods. IEEE. 18–23 (2019) S.P. Dewi, A.L. Prasasti, B. Irawan, The study of baby crying analysis using MFCC and LFCC in different classification methods. IEEE. 18–23 (2019)
15.
Zurück zum Zitat G.Z. Felipe, R.L. Aguiar, Y.M. Costa, C.N. Silla, S. Brahnam, L. Nanni, S. McMurtrey, , in 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), Identification of infants’ cry motivation using spectrograms (IEEE, 2019), pp. 181–186 G.Z. Felipe, R.L. Aguiar, Y.M. Costa, C.N. Silla, S. Brahnam, L. Nanni, S. McMurtrey, , in 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), Identification of infants’ cry motivation using spectrograms (IEEE, 2019), pp. 181–186
16.
Zurück zum Zitat E. Fenil, G. Manogaran, G.N. Vivekananda, T. Thanjaivadivel, S. Jeeva, A. Ahilan, Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Comput. Networks. 151, 191–200 (2019)CrossRef E. Fenil, G. Manogaran, G.N. Vivekananda, T. Thanjaivadivel, S. Jeeva, A. Ahilan, Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Comput. Networks. 151, 191–200 (2019)CrossRef
17.
Zurück zum Zitat R. Jahangir, CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting. Eng. Rep. e12786 (2023) R. Jahangir, CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting. Eng. Rep. e12786 (2023)
18.
Zurück zum Zitat C. Ji, X. Xiao, S. Basodi, Y. Pan, in 2019 International conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData), IEEE Deep learning for asphyxiated infant cry classification based on acoustic features and weighted prosodic features (2019), pp. 1233–1240 C. Ji, X. Xiao, S. Basodi, Y. Pan, in 2019 International conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData), IEEE Deep learning for asphyxiated infant cry classification based on acoustic features and weighted prosodic features (2019), pp. 1233–1240
19.
Zurück zum Zitat A. Kachhi, S. Chaturvedi, H.A. Patil, D.K. Singh, in 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP), Data augmentation for infant cry classification (IEEE, 2022), pp. 433–437 A. Kachhi, S. Chaturvedi, H.A. Patil, D.K. Singh, in 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP), Data augmentation for infant cry classification (IEEE, 2022), pp. 433–437
20.
Zurück zum Zitat Z. Khalilzad, C. Tadj, Using CCA-fused cepstral features in a deep learning-based cry diagnostic system for detecting an ensemble of pathologies in newborns. Diagn. 13(5), 879 (2023)CrossRef Z. Khalilzad, C. Tadj, Using CCA-fused cepstral features in a deep learning-based cry diagnostic system for detecting an ensemble of pathologies in newborns. Diagn. 13(5), 879 (2023)CrossRef
21.
Zurück zum Zitat Y. Kheddache, C. Tadj, Identification of diseases in newborns using advanced acoustic features of cry signals. Biomed. Signal Process. Control 50, 35–44 (2019)CrossRef Y. Kheddache, C. Tadj, Identification of diseases in newborns using advanced acoustic features of cry signals. Biomed. Signal Process. Control 50, 35–44 (2019)CrossRef
22.
Zurück zum Zitat K.B. Shah, S. Visalakshi, R. Panigrahi, Seven class solid waste management-hybrid features based deep neural network. Int. J. Syst. Design Comput. 01(01), 1–10 (2023) K.B. Shah, S. Visalakshi, R. Panigrahi, Seven class solid waste management-hybrid features based deep neural network. Int. J. Syst. Design Comput. 01(01), 1–10 (2023)
23.
Zurück zum Zitat Kristian, Y., Simogiarto, N., Sampurna, M.T.A., Hanindito, E., Visuddho, V.: Ensemble of multimodal deep learning autoencoder for infant cry and pain detection. F1000Research. 11, 359 (2023). Kristian, Y., Simogiarto, N., Sampurna, M.T.A., Hanindito, E., Visuddho, V.: Ensemble of multimodal deep learning autoencoder for infant cry and pain detection. F1000Research. 11, 359 (2023).
24.
Zurück zum Zitat S. Lahmiri, C. Tadj, C. Gargour, S. Bekiros, Deep learning systems for automatic diagnosis of infant cry signals. Chaos Solitons Fractals 154, 111700 (2022)CrossRef S. Lahmiri, C. Tadj, C. Gargour, S. Bekiros, Deep learning systems for automatic diagnosis of infant cry signals. Chaos Solitons Fractals 154, 111700 (2022)CrossRef
25.
Zurück zum Zitat L. Liu, Y. Li, K. Kuo, in 2018 International Conference on Information and Computer Technologies (ICICT), Infant cry signal detection, pattern extraction and recognition (IEEE, 2018), pp. 159–163 L. Liu, Y. Li, K. Kuo, in 2018 International Conference on Information and Computer Technologies (ICICT), Infant cry signal detection, pattern extraction and recognition (IEEE, 2018), pp. 159–163
26.
Zurück zum Zitat F.S. Matikolaie, C. Tadj, Machine learning-based cry diagnostic system for identifying septic newborns. J. Voice (2022) F.S. Matikolaie, C. Tadj, Machine learning-based cry diagnostic system for identifying septic newborns. J. Voice (2022)
27.
Zurück zum Zitat P. Naveen, P. Sivakumar, A deep convolution neural network for facial expression recognition. J. Curr. Sci. Technol. 11(3), 402–410 (2021) P. Naveen, P. Sivakumar, A deep convolution neural network for facial expression recognition. J. Curr. Sci. Technol. 11(3), 402–410 (2021)
28.
Zurück zum Zitat T. Ozseven, Infant cry classification by using different deep neural network models and hand-crafted features. Biomed. Signal Process. Control 83, 104648 (2023)CrossRef T. Ozseven, Infant cry classification by using different deep neural network models and hand-crafted features. Biomed. Signal Process. Control 83, 104648 (2023)CrossRef
29.
Zurück zum Zitat S. Ramasamy, A. Selvarajan, V. Kaliyaperumal, P. Aruchamy, A hybrid location-dependent ultra convolutional neural network-based vehicle number plate recognition approach for intelligent transportation systems. Concurr. Comput. Pract. Exper. 35(8), e7615 (2023)CrossRef S. Ramasamy, A. Selvarajan, V. Kaliyaperumal, P. Aruchamy, A hybrid location-dependent ultra convolutional neural network-based vehicle number plate recognition approach for intelligent transportation systems. Concurr. Comput. Pract. Exper. 35(8), e7615 (2023)CrossRef
30.
Zurück zum Zitat A. Rosales-Pérez, C.A. Reyes-García, J.A. Gonzalez, O.F. Reyes-Galaviz, H.J. Escalante, S. Orlandi, Classifying infant cry patterns by the genetic selection of a fuzzy model. Biomed. Signal Process. Control 17, 38–46 (2015)CrossRef A. Rosales-Pérez, C.A. Reyes-García, J.A. Gonzalez, O.F. Reyes-Galaviz, H.J. Escalante, S. Orlandi, Classifying infant cry patterns by the genetic selection of a fuzzy model. Biomed. Signal Process. Control 17, 38–46 (2015)CrossRef
31.
Zurück zum Zitat Y.D. Rosita, H. Junaedi, in 2016 2nd International Conference on Science and Technology Computer (ICST), Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network (IEEE, 2016), pp. 160–166 Y.D. Rosita, H. Junaedi, in 2016 2nd International Conference on Science and Technology Computer (ICST), Infant's cry sound classification using Mel-Frequency Cepstrum Coefficients feature extraction and Backpropagation Neural Network (IEEE, 2016), pp. 160–166
32.
Zurück zum Zitat M. Severini, D. Ferretti, E. Principi, S. Squartini, Automatic detection of cry sounds in neonatal intensive care units by using deep learning and acoustic scene simulation. IEEE Access 7, 51982–51993 (2019)CrossRef M. Severini, D. Ferretti, E. Principi, S. Squartini, Automatic detection of cry sounds in neonatal intensive care units by using deep learning and acoustic scene simulation. IEEE Access 7, 51982–51993 (2019)CrossRef
33.
Zurück zum Zitat R. Subha, A. Haldorai, A. Ramu, An optimal approach to enhance context aware description administration service for cloud robots in a deep learning environment. Wireless Pers. Commun. 117, 3343–3358 (2021)CrossRef R. Subha, A. Haldorai, A. Ramu, An optimal approach to enhance context aware description administration service for cloud robots in a deep learning environment. Wireless Pers. Commun. 117, 3343–3358 (2021)CrossRef
34.
Zurück zum Zitat K. Sujatha, G. Nalinashini, A. Ganesan, A. Kalaivani, K. Sethil, R. Hari, F.A.X. Bronson, K. Bhaskar, in Implementation of Smart Healthcare Systems Using AI, IoT, and Blockchain, Internet of medical things for abnormality detection in infants using mobile phone app with cry signal analysis (2023), pp. 169–191 K. Sujatha, G. Nalinashini, A. Ganesan, A. Kalaivani, K. Sethil, R. Hari, F.A.X. Bronson, K. Bhaskar, in Implementation of Smart Healthcare Systems Using AI, IoT, and Blockchain, Internet of medical things for abnormality detection in infants using mobile phone app with cry signal analysis (2023), pp. 169–191
35.
Zurück zum Zitat K. Teeravajanadet, N. Siwilai, K. Thanaselanggul, N. Ponsiricharoenphan, S. Tungjitkusolmun, P. Phasukkit, in 2019 12th Biomedical Engineering International Conference (BMEiCON) IEEE, An infant cry recognition based on convolutional neural network method (2019), pp. 1–4 K. Teeravajanadet, N. Siwilai, K. Thanaselanggul, N. Ponsiricharoenphan, S. Tungjitkusolmun, P. Phasukkit, in 2019 12th Biomedical Engineering International Conference (BMEiCON) IEEE, An infant cry recognition based on convolutional neural network method (2019), pp. 1–4
36.
Zurück zum Zitat S. Upadhyay, R.B. Lincy, R.B. Jeyavathana, A. Gopatoti, In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), IEEE 877–883 (2022). S. Upadhyay, R.B. Lincy, R.B. Jeyavathana, A. Gopatoti, In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), IEEE 877–883 (2022).
37.
Zurück zum Zitat V. Vaishnavi, P.S. Dhanaselvam, Premature infant cry signal prediction and classification via dense convolution neural network. J. Intell. Fuzzy Syst. 42, 1–14 (2022) V. Vaishnavi, P.S. Dhanaselvam, Premature infant cry signal prediction and classification via dense convolution neural network. J. Intell. Fuzzy Syst. 42, 1–14 (2022)
38.
Zurück zum Zitat P.D.R. Vincent, K. Srinivasan, C.Y. Chang, Deep learning assisted premature infant cry classification via support vector machine models. Public Health Front. 9, 670352 (2021)CrossRef P.D.R. Vincent, K. Srinivasan, C.Y. Chang, Deep learning assisted premature infant cry classification via support vector machine models. Public Health Front. 9, 670352 (2021)CrossRef
39.
Zurück zum Zitat S. Wang, J. Du, Y. Wang, in National Conference on Man-Machine Speech Communication, Baby cry recognition based on acoustic segment model (2023), pp. 16–29 S. Wang, J. Du, Y. Wang, in National Conference on Man-Machine Speech Communication, Baby cry recognition based on acoustic segment model (2023), pp. 16–29
40.
Zurück zum Zitat Y. Zayed, A. Hasasneh, C. Tadj, Infant cry signal diagnostic system using deep learning and fused features. Diagnostics. 13(12), 2107 (2023)CrossRef Y. Zayed, A. Hasasneh, C. Tadj, Infant cry signal diagnostic system using deep learning and fused features. Diagnostics. 13(12), 2107 (2023)CrossRef
Metadaten
Titel
Premature Infant Cry Classification via Elephant Herding Optimized Convolutional Gated Recurrent Neural Network
verfasst von
V. Vaishnavi
M. Braveen
N. Muthukumaran
P. Poonkodi
Publikationsdatum
04.07.2024
Verlag
Springer US
Erschienen in
Circuits, Systems, and Signal Processing / Ausgabe 10/2024
Print ISSN: 0278-081X
Elektronische ISSN: 1531-5878
DOI
https://doi.org/10.1007/s00034-024-02764-5