COVID-19 CNN MFCC classifier

High accuracy classification of COVID-19 coughs using Mel-frequency cepstral coefficients and a Convolutional Neural Network with a use case for smart home devices. Diagnosing COVID-19 early in domestic settings is possible through smart home devices that can classify audio input of coughs, and determine whether they are COVID-19. Research is currently sparse in this area and data is difficult to obtain. How- ever, a few small data collection projects have en- abled audio classification research into the application of different machine learning classification algorithms, including Logistic Regression (LR), Support Vector Machines (SVM), and Convolution Neural Networks (CNN). We show here that a CNN using audio converted to Mel-frequency cepstral coefficient spectrogram images as input can achieve high accuracy results; with classification of validation data scoring an accuracy of 97.5% cor- rect classification of covid and not covid labelled audio. The work here provides a proof of concept that high accuracy can be achieved with a small dataset, which can have a significant impact in this area. The results are highly encouraging and provide further opportunities for research by the academic community on this important topic. Preprint: https://www.researchgate.net/publication/343376336_High_accuracy_classification_of_COVID-19_coughs_using_Mel-frequency_cepstral_coefficients_and_a_Convolutional_Neural_Network_with_a_use_case_for_smart_home_devices

基于梅尔频率倒谱系数（Mel-frequency cepstral coefficients）与卷积神经网络（Convolutional Neural Network）的新冠咳嗽高精度分类：面向智能家居设备的应用场景。借助可对咳嗽音频输入进行分类并判断是否为新冠阳性咳嗽的智能家居设备，可在居家环境中实现新冠的早期诊断。当前该领域研究相对匮乏，且数据获取难度较大。不过，已有若干小规模数据采集项目开展，推动了针对多种机器学习分类算法的音频分类研究，所涉算法包括逻辑回归（Logistic Regression，LR）、支持向量机（Support Vector Machines，SVM）与卷积神经网络（CNN）。本研究表明，以转换为梅尔频率倒谱系数频谱图的音频作为输入的卷积神经网络，可实现高精度分类效果；其在验证集上对标注为新冠阳性与阴性的咳嗽音频的分类准确率可达97.5%。本研究完成了概念验证，证明借助小规模数据集即可实现高精度分类，这对该领域将产生重要影响。本次研究结果令人备受鼓舞，也为学术界围绕这一重要主题开展后续研究提供了新的方向。预印本链接：https://www.researchgate.net/publication/343376336_High_accuracy_classification_of_COVID-19_coughs_using_Mel-frequency_cepstral_coefficients_and_a_Convolutional_Neural_Network_with_a_use_case_for_smart_home_devices