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.

婴儿啼哭信号的传统研究更多地集中在基于非病理学的婴儿分类上。在本文中，我们介绍了一种非侵入式医疗保健系统，该系统可以对不干净嘈杂的婴儿啼哭信号进行声学分析，以定量地提取和测量某些啼哭特征，并仅根据他们的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，动态MFCC特征以及静态梅尔频率倒谱系数（MFCC）被选择并提取用于呼气和吸气哭声，以产生判别性和信息性特征向量。下一个，我们通过使用Boosting Mixture Learning（BML）方法引入一种新颖的想法以从高斯混合模型通用背景模型（GMM-UBM）分别派生健康或病理子类模型的方式，为每种哭声类型和病理状况创建独特的哭声模式）。我们的新生儿基于哭泣的诊断系统（NCDS）具有分层方案，该分层方案是单个分类器的树状组合。此外，对提议的基于呼气和吸气的基于哭泣的子系统进行评分级别的融合，以做出更可靠的决策。实验结果表明，作为参考方法，自适应的BML方法的错误率低于贝叶斯方法或最大后验概率（MAP）自适应方法。