Abstract
Mel frequency cepstral coefficients are one of the most prominent sets of primary features of an audio signal which are used for speech detection and cough analysis. This paper presents a new method that can overcome some of the common problems faced by using MFCCs for cough detection. In the proposed method, the most prominent part of the cough sample (HCP) is extracted and used to obtain the MFCC vectors of that particular window. These HCP MFCC vectors work as a standard comparison index for all cough samples to detect any respiratory disorders. The evaluation of the proposed method is done using 40 samples of COVID-19 patients of which 20 are positive and 20 are negative. The accuracy of the proposed method is compared with that of the standard MFCC method for the same set of samples. The proposed HCP MFCC method produces results that are 7.84% more accurate than the standard method. By bringing a standard set of comparing features that can work for almost all use cases, this method can be used as a quick identifying tool for various respiratory diseases.
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Monish Singam, S., Rajesh Menon, P., Ezhilan, M., Arjun, B.R., Kalaivani, S. (2023). Respiratory Disease Diagnosis with Cough Sound Analysis. In: Pati, B., Panigrahi, C.R., Mohapatra, P., Li, KC. (eds) Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Lecture Notes in Networks and Systems, vol 428. Springer, Singapore. https://doi.org/10.1007/978-981-19-2225-1_1
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