自動咳嗽檢測的音頻特徵評估（Sound）
- 2020 年 1 月 6 日
- 筆記
本文討論了僅使用錄音來檢測咳嗽的問題，最終目的是對患有呼吸系統疾病，特別是粘稠物阻塞症的患者的病理程度進行量化和鑒定。本文提出了一種描述音頻信號各個方面的大型音頻特徵集。這些特徵分兩個步驟進行評估。首先，利用相互的信息度量來評估它們的內部潛力和冗餘度。其次，利用人工神經網絡、高斯混合模型和支持向量機這三種分類器來確定它們的效率。同樣的，本文也研究了特徵維數和分類器複雜度的影響。
原文題目：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
原文鏈接： https://arxiv.org/abs/2001.00580
