This paper addresses the issue of cough detection using only audio recordings, with the ultimate goal of quantifying and qualifying the degree of pathology for patients suffering from respiratory diseases, notably mucoviscidosis. A large set of audio features describing various aspects of the audio signal is proposed. These features are assessed in two steps. First, their intrisic potential and redundancy are evaluated using mutual information-based measures. Secondly, their efficiency is confirmed relying on three classifiers: Artificial Neural Network, Gaussian Mixture Model and Support Vector Machine. The influence of both the feature dimension and the classifier complexity are also investigated.

中文翻译：

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自动咳嗽检测的音频特征评估

本文解决了仅使用录音检测咳嗽的问题，最终目标是量化和限定患有呼吸系统疾病，尤其是黏液粘稠病的患者的病理程度。提出了大量描述音频信号各个方面的音频特征。这些功能分两个步骤进行评估。首先，使用基于互信息的措施评估它们的内在潜力和冗余。其次，它们的效率依赖于三个分类器：人工神经网络、高斯混合模型和支持向量机。还研究了特征维度和分类器复杂度的影响。