Abou-Abbas Lina, Tadj Chakib, Fersaie Hesam Alaie
Department of Electrical Engineering, École de Technologie Supérieure, Quebec University, 1100 Rue Notre Dame Ouest, Montréal, Quebec H3C 1K3, Canada.
J Acoust Soc Am. 2017 Sep;142(3):1318. doi: 10.1121/1.5001491.
The detection of cry sounds is generally an important pre-processing step for various applications involving cry analysis such as diagnostic systems, electronic monitoring systems, emotion detection, and robotics for baby caregivers. Given its complexity, an automatic cry segmentation system is a rather challenging topic. In this paper, a framework for automatic cry sound segmentation for application in a cry-based diagnostic system has been proposed. The contribution of various additional time- and frequency-domain features to increase the robustness of a Gaussian mixture model/hidden Markov model (GMM/HMM)-based cry segmentation system in noisy environments is studied. A fully automated segmentation algorithm to extract cry sound components, namely, audible expiration and inspiration, is introduced and is grounded on two approaches: statistical analysis based on GMMs or HMMs classifiers and a post-processing method based on intensity, zero crossing rate, and fundamental frequency feature extraction. The main focus of this paper is to extend the systems developed in previous works to include a post-processing stage with a set of corrective and enhancing tools to improve the classification performance. This full approach allows to precisely determine the start and end points of the expiratory and inspiratory components of a cry signal, EXP and INSV, respectively, in any given sound signal. Experimental results have indicated the effectiveness of the proposed solution. EXP and INSV detection rates of approximately 94.29% and 92.16%, respectively, were achieved by applying a tenfold cross-validation technique to avoid over-fitting.
对于各种涉及哭声分析的应用，如诊断系统、电子监测系统、情感检测以及面向婴儿护理的机器人技术等，哭声检测通常是一个重要的预处理步骤。鉴于其复杂性，自动哭声分割系统是一个颇具挑战性的课题。本文提出了一种用于基于哭声的诊断系统的自动哭声分割框架。研究了各种额外的时域和频域特征对提高基于高斯混合模型/隐马尔可夫模型（GMM/HMM）的哭声分割系统在噪声环境中的鲁棒性的贡献。介绍了一种用于提取哭声成分（即可听呼气和吸气）的全自动分割算法，该算法基于两种方法：基于GMM或HMM分类器的统计分析以及基于强度、过零率和基频特征提取的后处理方法。本文的主要重点是扩展先前工作中开发的系统，使其包括一个带有一组校正和增强工具的后处理阶段，以提高分类性能。这种完整的方法能够在任何给定的声音信号中，分别精确确定哭声信号的呼气和吸气成分（EXP和INSV）的起始点和终点。实验结果表明了所提出解决方案的有效性。通过应用十折交叉验证技术以避免过拟合，分别实现了约94.29%和92.16%的EXP和INSV检测率。
