Vol. 18 No. 1 (2021): Edisi Februari 2021
Artificial Intelligence

Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

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  • Heriyanto Heriyanto
Heriyanto Heriyanto
UPN "Veteran" Yogyakarta
Bio
DOI: https://doi.org/10.31315/telematika.v18i1.4495

Published 2021-03-16

Keywords

  • extraction of features,
  • features,
  • frames,
  • cepstral coefficient,
  • linear

How to Cite

Heriyanto, H. (2021). Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection. Telematika: Jurnal Informatika Dan Teknologi Informasi, 18(1), 88–105. https://doi.org/10.31315/telematika.v18i1.4495

Abstract

Purpose:

Select the right features on the frame for good accuracy

Design/methodology/approach:

Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) Features

Findings/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.
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