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

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

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...

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Bibliographic Details
Main Author: Heriyanto Heriyanto
Format: Article
Language:Indonesian
Published: Universitas Pembangunan Nasional "Veteran" Yogyakarta 2021-03-01
Series:Telematika
Subjects:
extraction of features
features
frames
cepstral coefficient
linear
Online Access:http://jurnal.upnyk.ac.id/index.php/telematika/article/view/4495
id doaj-d828da9c8eb447ec9d3fecc077f56447
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spelling doaj-d828da9c8eb447ec9d3fecc077f564472021-03-16T16:55:11ZindUniversitas Pembangunan Nasional "Veteran" YogyakartaTelematika1829-667X2460-90212021-03-011818810510.31315/telematika.v18i1.44953118Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature SelectionHeriyanto Heriyanto0UPN "Veteran" YogyakartaPurpose: 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.http://jurnal.upnyk.ac.id/index.php/telematika/article/view/4495extraction of featuresfeaturesframescepstral coefficientlinear
collection DOAJ
language Indonesian
format Article
sources DOAJ
author Heriyanto Heriyanto
spellingShingle Heriyanto Heriyanto
Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
Telematika
extraction of features
features
frames
cepstral coefficient
linear
author_facet Heriyanto Heriyanto
author_sort Heriyanto Heriyanto
title Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
title_short Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
title_full Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
title_fullStr Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
title_full_unstemmed Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection
title_sort good morning to good night greeting classification using mel frequency cepstral coefficient (mfcc) feature extraction and frame feature selection
publisher Universitas Pembangunan Nasional "Veteran" Yogyakarta
series Telematika
issn 1829-667X
2460-9021
publishDate 2021-03-01
description 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.
topic extraction of features
features
frames
cepstral coefficient
linear
url http://jurnal.upnyk.ac.id/index.php/telematika/article/view/4495
work_keys_str_mv AT heriyantoheriyanto goodmorningtogoodnightgreetingclassificationusingmelfrequencycepstralcoefficientmfccfeatureextractionandframefeatureselection
_version_ 1714783457301757952

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