论文标题
基于MFCC的相关分析研究Covid-19声音的相似性
Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC
论文作者
论文摘要
最近，与研究人员和科学家一起，在医院，诊所和实验室等前线工作的人们进行了一项艰巨的工作，他们也在与Covid-19-19-19的斗争中付出了巨大的努力。由于病毒的荒谬传播，人工智能的整合通过实施自动语音识别（ASR）和深度学习算法的基本原理，在卫生部门中占据了相当大的作用。在本文中，我们说明了语音信号处理在提取COVID-19和非COVID-19的样品的Mel频率Cepstral系数（MFCC）中的重要性，并使用Pearson相关系数找到了它们的关系。我们的结果表明，在不同的19009咳嗽和呼吸声音之间，MFCC的相似性很高，而COVID-19和非covid-19样本之间的MFCC语音更强。此外，我们的结果是初步的，并且有可能将Covid-19患者的声音排除在诊断疾病时进一步处理。
Recently there has been a formidable work which has been put up from the people who are working in the frontlines such as hospitals, clinics, and labs alongside researchers and scientists who are also putting tremendous efforts in the fight against COVID-19 pandemic. Due to the preposterous spread of the virus, the integration of the artificial intelligence has taken a considerable part in the health sector, by implementing the fundamentals of Automatic Speech Recognition (ASR) and deep learning algorithms. In this paper, we illustrate the importance of speech signal processing in the extraction of the Mel-Frequency Cepstral Coefficients (MFCCs) of the COVID-19 and non-COVID-19 samples and find their relationship using Pearson correlation coefficients. Our results show high similarity in MFCCs between different COVID-19 cough and breathing sounds, while MFCC of voice is more robust between COVID-19 and non-COVID-19 samples. Moreover, our results are preliminary, and there is a possibility to exclude the voices of COVID-19 patients from further processing in diagnosing the disease.
