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Classification of Infant Cries Using Dynamics of Epoch Features

机译:利用时代特征对婴儿哭声进行分类

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In this article, epoch-based dynamic features such as sequence of epoch interval values and epoch strength values are explored to classify infant cries. Epoch is the instant of significant excitation of the vocal tract system during the production of speech. For voiced speech, the most significant excitation takes place around the instant of glottal closure. The different types of infant cries considered in this work are hunger, pain, and wet diaper. In this work, epoch strength and epoch interval features are used to represent infant cry-specific information from the acoustic signal. In this study, the proposed features such as epoch interval and epoch strength values are determined using zero-frequency filter-based method. Gaussian mixture models (GMMs) are used to classify the above-mentioned cries from the features proposed in this work. GMMs are developed separately for each of the cries using the proposed features. The infant cry database collected under a telemedicine project at the Indian Institute of Technology Kharagpur has been used for this study. In the first step, infant cry recognition accuracy is investigated separately using epoch interval and epoch strength features. To enhance recognition performance, GMMs developed using various features are combined through score level fusion techniques. The recognition performance using a combination of evidence is found to be superior over individual systems.
机译:在本文中,探索了基于纪元的动态特征,例如纪元间隔值和纪元强度值的序列,以对婴儿哭声进行分类。时代是语音产生过程中声道系统明显兴奋的瞬间。对于浊音,最显着的激发发生在声门关闭的瞬间。在这项工作中考虑的婴儿哭声的不同类型是饥饿,疼痛和尿布湿。在这项工作中,时代强度和时代间隔特征被用来代表来自声信号的婴儿特定哭声信息。在这项研究中,使用基于零频滤波器的方法来确定所提议的特征,例如历元间隔和历元强度值。高斯混合模型(GMM)用于根据这项工作提出的特征对上述哭声进行分类。使用建议的功能为每个哭声单独开发GMM。在印度哈拉格普尔技术学院的远程医疗项目下收集的婴儿啼哭数据库已用于这项研究。第一步,使用历时间隔和历时强​​度特征分别研究婴儿哭泣识别的准确性。为了提高识别性能,使用各种功能开发的GMM通过分数级融合技术进行了组合。发现使用证据组合的识别性能优于单个系统。

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