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

Authors

  • Heriyanto Heriyanto UPN "Veteran" Yogyakarta

DOI:

https://doi.org/10.31315/telematika.v18i1.4495

Keywords:

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

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.

Author Biography

Heriyanto Heriyanto, UPN "Veteran" Yogyakarta

FTI UPN "Veteran" Yk

prodi Informatika

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Published

2021-03-16

Issue

Section

Artificial Intelligence