论文标题
评估自动咳嗽检测的音频功能
Assessment of Audio Features for Automatic Cough Detection
论文作者
论文摘要
本文仅使用音频记录解决了咳嗽检测的问题，其最终目的是量化和合格患有呼吸道疾病的患者的病理程度，尤其是粘膜念珠菌病。提出了描述音频信号各个方面的大量音频功能。这些功能通过两个步骤进行评估。首先，使用基于信息的措施评估它们的主体潜力和冗余性。其次，依赖三个分类器确认了它们的效率：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性的影响。
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.
