The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models.
翻译:去年,临床科学家和医学研究者小组对呼吸道健康分类问题给予了很好的注意,以诊断COVID-19疾病;迄今为止,各种人工智能模型(AI)进入现实世界,从声音/声音、咳嗽和呼吸等人类产生的声音中检测COVID-19疾病; 革命神经网络模型(CNN)用于解决基于人工智能(AI)的机器上的许多现实世界问题; 在这方面,建议并采用CNN(1D)来诊断COVID-19的呼吸道疾病,从声音、咳嗽和呼吸等人类呼吸道声音中诊断出一种层面(1D)CNN; 采用基于增强的机制来改进COVID-19声音数据集的处理前性能,并利用1D革命网络自动进行COVID-19疾病诊断; 此外,还采用DADE(Data Denoision Autoccoder)技术来产生深刻的声学特征,如向1DCNNNC系统输入功能,而不是采用MFCC的标准输入(M-C)的精确度和性能比以往的精确度。