自动咳嗽检测的音频特征评估（Sound）

• 2020 年 1 月 6 日
• 筆記

本文讨论了仅使用录音来检测咳嗽的问题，最终目的是对患有呼吸系统疾病，特别是粘稠物阻塞症的患者的病理程度进行量化和鉴定。本文提出了一种描述音频信号各个方面的大型音频特征集。这些特征分两个步骤进行评估。首先，利用相互的信息度量来评估它们的内部潜力和冗余度。其次，利用人工神经网络、高斯混合模型和支持向量机这三种分类器来确定它们的效率。同样的，本文也研究了特征维数和分类器复杂度的影响。

原文题目：Sound：Assessment of Audio Features for Automatic Cough Detection

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.

原文作者：Thomas Drugman,Jerome Urbain,Thierry Dutoit

原文链接： https://arxiv.org/abs/2001.00580