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首页> 外文期刊>Journal of voice: official journal of the Voice Foundation >Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques
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Expiratory and Inspiratory Cries Detection Using Different Signals' Decomposition Techniques

机译：使用不同信号的分解技术进行呼气和吸气呼吸探测

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摘要

This paper addresses the problem of automatic cry signal segmentation for the purposes of infant cry analysis. The main goal is to automatically detect expiratory and inspiratory phases from recorded cry signals. The approach used in this paper is made up of three stages: signal decomposition, features extraction, and classification. In the first stage, short-time Fourier transform, empirical mode decomposition (EMD), and wavelet packet transform have been considered. In the second stage, various set of features have been extracted, and in the third stage, two supervised learning methods, Gaussian mixture models and hidden Markov models, with four and five states, have been discussed as well. The main goal of this work is to investigate the EMD performance and to compare it with the other standard decomposition techniques. A combination of two and three intrinsic mode functions (IMFs) that resulted from EMD has been used to represent cry signal. The performance of nine different segmentation systems has been evaluated. The experiments for each system have been repeated several times with different training and testing datasets, randomly chosen using a 10-fold cross-validation procedure. The lowest global classification error rates of around 8.9% and 11.06% have been achieved using a Gaussian mixture models classifier and a hidden Markov models classifier, respectively. Among all IMF combinations, the winner combination is IMF3+IMF4+IMF5.
机译：本文针对婴儿哭声分析中的哭声信号自动分割问题进行了研究。主要目标是从记录的哭声信号中自动检测呼气和吸气相位。本文采用的方法分为三个阶段：信号分解、特征提取和分类。第一阶段考虑了短时傅里叶变换、经验模态分解（EMD）和小波包变换。在第二阶段中，我们提取了各种特征集；在第三阶段中，我们还讨论了两种有监督学习方法：高斯混合模型和隐马尔可夫模型，以及四种和五种状态。这项工作的主要目标是研究EMD性能，并将其与其他标准分解技术进行比较。由EMD产生的两个和三个固有模式函数（IMF）的组合已被用于表示哭声信号。对九种不同分割系统的性能进行了评估。每个系统的实验都用不同的训练和测试数据集重复了几次，使用10倍交叉验证程序随机选择。使用高斯混合模型分类器和隐马尔可夫模型分类器分别获得了约8.9%和11.06%的最低全局分类错误率。在所有IMF组合中，赢家组合是IMF3+IMF4+IMF5。

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