Classification of asphyxia infant cry using hybrid speech features and deep learning models

Author:

Ting Hua-Nong,Choo Yao-Mun,Ahmad Kamar Azanna

Funder

Universiti Malaya

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference31 articles.

1. Automatic diagnosis of asphyxia infant cry signals using wavelet based mel frequency cepstrum features;Badreldine,2018

2. Using cloud and fog computing for large scale IoT-based urban sound classification;Baucas;Simulation Modelling Practice and Theory,2020

3. Baby cry detection: deep learning and classical approaches;Cohen,2020

4. Cohen, R. a. R. D. a. Z. J. a. I. H. a. L. Y. (2020). Baby Cry Detection: Deep Learning and Classical Approaches. 10.1007/978-3-030-31764-5_7.

5. Click-event sound detection in automotive industry using machine/deep learning;Espinosa;Applied Soft Computing,2021

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3. Effective Infant Cry Signal Analysis and Reasoning using IARO based Leaky Bi-LSTM Model;Computer Speech & Language;2024-01

4. Classification of Normal vs. Pathological Infant Cries Using Morse Wavelets;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31

5. CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting;Engineering Reports;2023-10-03

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