School of Mechatronic Engineering, UniMAP, Arau, Malaysia.
J Med Syst. 2012 Jun;36(3):1309-15. doi: 10.1007/s10916-010-9591-z. Epub 2010 Sep 16.
Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.
对婴儿哭声信号进行声学分析已被证明是自动检测婴儿病理状态的优秀工具。本文研究了参数加权线性预测倒谱系数 (LPCC) 的应用，以提供婴儿哭声信号的稳健表示。考虑了三类婴儿哭声信号，即正常哭声信号、耳聋婴儿哭声信号和窒息婴儿哭声信号。提出了一种概率神经网络 (PNN) 来对婴儿哭声信号进行分类，分为正常哭声和病理哭声。PNN 采用不同的扩展因子或平滑参数进行训练，以获得更好的分类准确性。实验结果表明，所提出的特征和分类算法的分类准确率非常高，超过 98%，这表明该方法可用于帮助医学专业人员从哭声信号诊断婴儿的病理状态。
