Traditional studies of infant cry signals focus more on non-pathology-based classification of infants. In this paper, we introduce a noninvasive health care system that performs acoustic analysis of unclean noisy infant cry signals to extract and measure certain cry characteristics quantitatively and classify healthy and sick newborn infants according to only their cries. In the conduct of this newborn cry-based diagnostic system, the dynamic MFCC features along with static Mel-Frequency Cepstral Coefficients (MFCCs) are selected and extracted for both expiratory and inspiratory cry vocalizations to produce a discriminative and informative feature vector. Next, we create a unique cry pattern for each cry vocalization type and pathological condition by introducing a novel idea using the Boosting Mixture Learning (BML) method to derive either healthy or pathology subclass models separately from the Gaussian Mixture Model-Universal Background Model (GMM-UBM). Our newborn cry-based diagnostic system (NCDS) has a hierarchical scheme that is a treelike combination of individual classifiers. Moreover, a score-level fusion of the proposed expiratory and inspiratory cry-based subsystems is performed to make a more reliable decision. The experimental results indicate that the adapted BML method has lower error rates than the Bayesian approach or the maximum a posteriori probability (MAP) adaptation approach when considered as a reference method.

婴儿哭泣信号的传统研究更多地关注基于非病理学的婴儿分类。在本文中，我们介绍了一种无创医疗保健系统，该系统对不干净的嘈杂婴儿哭声信号进行声学分析，以定量提取和测量某些哭声特征，并仅根据哭声对健康和患病的新生儿进行分类。在这个基于新生儿哭声的诊断系统中，选择并提取呼气和吸气哭声的动态 MFCC 特征以及静态梅尔倒谱系数 (MFCC)，以产生有辨别力和信息丰富的特征向量。接下来，我们通过引入一种新颖的想法，使用增强混合学习 (BML) 方法，从高斯混合模型通用背景模型 (GMM) 中分别导出健康或病理子类模型，为每种哭泣发声类型和病理状况创建独特的哭泣模式-UBM）。我们的新生儿哭声诊断系统 (NCDS) 具有分层方案，该方案是各个分类器的树状组合。此外，对所提出的基于呼气和吸气的子系统进行评分级融合，以做出更可靠的决策。实验结果表明，当被视为参考方法时，自适应 BML 方法比贝叶斯方法或最大后验概率 (MAP) 自适应方法具有更低的错误率。