Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
DOI:
https://doi.org/10.31315/telematika.v18i1.4495Keywords:
extraction of features, features, frames, cepstral coefficient, linearAbstract
Purpose:
Select the right features on the frame for good accuracyDesign/methodology/approach:
Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:
The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:
Selection of the appropriate features on the frame.Downloads
Published
2021-03-16
Issue
Section
Artificial Intelligence