本文回顾了近50年来病理性婴儿哭声分析和分类的研究工作。文献综述主要包括早期临床诊断的需要和作用,从哭泣样本中检测到的病理,病理性哭泣信号数据采集中的挑战,信号处理技术,和信号分类器。信号处理技术包括预处理,从域中提取特征,比如时间,光谱,时间-频率,韵律,小波,etc,和用于选择主要特征的特征选择。文献涵盖了传统的机器学习分类器,比如贝叶斯网络,决策树,K-最近邻,支持向量机,高斯混合模型,etc,最近增加了神经网络模型,比如卷积神经网络,回归神经网络,概率神经网络,图神经网络,等。列出了病理哭泣识别和分类的重要实验结果,以进行比较。最后,它提出了数据库准备方向的未来研究,特征分析和提取,神经网络分类器提供了一个非侵入性和鲁棒性的自动婴儿哭声分析模型。
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.