• Abhay Patil
• Pallavi Thorat
• Aniruddha P. Kshirsagar

In this research paper, we have proposed a flexible machine learning system to detect the COVID-19 symptoms using the methodology of the audio search engine via audio fingerprinting. The algorithm is noise and contortion resistant, computationally yielding, and massively scalable, capable of quickly identifying a quick segment of breathing and coughing sound patterns captured through a cellphone microphone in the presence of foreground voices and other dominant noise, and through voice codec compression, out of a database of over thousands of breathing/coughing noise samples provided by many research organizations worldwide including European healthcare labs. The proposed idea of this system will help to detect the typically unique symptoms of this disorder in an efficient form along with cheap rates as compared to the RNA Extraction kits/Rapid Antibody Test kits. The algorithm uses a combinatorial hashed time-frequency constellation analysis of the audio clip, resulting in unusual properties such as transparency, in which multiple tracks mixed may each be identified.

Audio fingerprint, COVID-19, Machine Learning, Spectrogram, Hashing

"Alveolar Ailment Identifier Utilizing Audio Fingerprinting Methodology for Efficacious Covid-19 Symptoms Sensing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.c962-c967, August-2021, Available :http://www.jetir.org/papers/JETIR2108368.pdf

"Alveolar Ailment Identifier Utilizing Audio Fingerprinting Methodology for Efficacious Covid-19 Symptoms Sensing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppc962-c967, August-2021, Available at : http://www.jetir.org/papers/JETIR2108368.pdf