A Narrative Review on Different Novel Machine Learning Techniques for Detecting Pathologies in Infants From Born Baby Cries.
J Voice
; 2024 May 06.
Article
en En
| MEDLINE
| ID: mdl-38714440
ABSTRACT
This paper reviews the research work on the analysis and classification of pathological infant cries in the last 50 years. The literature review mainly covers the need and role of early clinical diagnosis, pathologies detected from cry samples, challenges in pathological cry signal data acquisition, signal processing techniques, and signal classifiers. The signal processing techniques include preprocessing, feature extraction from domains, such as time, spectral, time-frequency, prosodic, wavelet, etc, and feature selection for selecting dominant features. Literature covers traditional machine learning classifiers, such as Bayesian networks, decision trees, K-nearest neighbor, support vector machine, Gaussian mixture model, etc, and recently added neural network models, such as convolutional neural networks, regression neural networks, probabilistic neural networks, graph neural networks, etc. Significant experimental results of pathological cry identification and classification are listed for comparison. Finally, it suggests future research in the direction of database preparation, feature analysis and extraction, neural network classifiers to provide a non-invasive and robust automatic infant cry analysis model.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
J Voice
Asunto de la revista:
OTORRINOLARINGOLOGIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos