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10.1016/j.patcog.2021.107999 http://scihub22266oqcxt.onion/10.1016/j.patcog.2021.107999 33967346!8086594!33967346     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit 2021 ; 117 (ä): 107999 Nephropedia Template TP {{tp|p=33967346|t=2021. Detection of COVID-19 from speech signal using bio-inspired based cepstral features |pdf=|usr=}}{{33967346}} gab.com Text Detection of COVID-19 from speech signal using bio-inspired based cepstral features JO: Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=33967346 #FOAvip The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared. Twit Text FOAVip Detection of COVID-19 from speech signal using bio-inspired based cepstral features ????Pattern Recognit http://www.kidney.de/pget.php?&tw=1&pmid=33967346 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33967346 #FOAvip English Wikipedia <ref>{{Cite pmid|33967346}}</ref> Detection of COVID-19 from speech signal using bio-inspired based cepstral features #MMPMID33967346 Dash TK; Mishra S; Panda G; Satapathy SC Pattern Recognit 2021[Sep]; 117 (ä): 107999 PMID33967346show ga The early detection of COVID-19 is a challenging task due to its deadly spreading nature and existing fear in minds of people. Speech-based detection can be one of the safest tools for this purpose as the voice of the suspected can be easily recorded. The Mel Frequency Cepstral Coefficient (MFCC) analysis of speech signal is one of the oldest but potential analysis tools. The performance of this analysis mainly depends on the use of conversion between normal frequency scale to perceptual frequency scale and the frequency range of the filters used. Traditionally, in speech recognition, these values are fixed. But the characteristics of speech signals vary from disease to disease. In the case of detection of COVID-19, mainly the coughing sounds are used whose bandwidth and properties are quite different from the complete speech signal. By exploiting these properties the efficiency of the COVID-19 detection can be improved. To achieve this objective the frequency range and the conversion scale of frequencies have been suitably optimized. Further to enhance the accuracy of detection performance, speech enhancement has been carried out before extraction of features. By implementing these two concepts a new feature called COVID-19 Coefficient (C-19CC) is developed in this paper. Finally, the performance of these features has been compared. äDetection of COVID-19 from speech signal using bio-inspired based ceps(epmc).pdf DeepDyve Pubget Overpricing