Open AccessComparison of Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction, With and Without Framing Feature Selection, to Test the Shahada Recitation
Author(s) -
Heriyanto Heriyanto,
Dyah Ayu Irawati
Publication year - 2021
Publication title -
rsf conference proceeding series. engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2809-6843
pISSN - 2809-6878
DOI - 10.31098/cset.v1i1.395
Subject(s) - mel frequency cepstrum , feature extraction , artificial intelligence , computer science , feature selection , pattern recognition (psychology) , frame (networking) , speech recognition , normalization (sociology) , feature (linguistics) , cepstrum , telecommunications , linguistics , philosophy , sociology , anthropology
Voice research for feature extraction using MFCC. Introduction with feature extraction as the first step to get features. Features need to be done further through feature selection. The feature selection in this research used the Dominant Weight feature for the Shahada voice, which produced frames and cepstral coefficients as the feature extraction. The cepstral coefficient was used from 0 to 23 or 24 cepstral coefficients. At the same time, the taken frame consisted of 0 to 10 frames or eleven frames. Voting as many as 300 samples of recorded voices were tested on 200 voices of both male and female voice recordings. The frequency used was 44.100 kHz 16-bit stereo. This research aimed to gain accuracy by selecting the right features on the frame using MFCC feature extraction and matching accuracy with frame feature selection using the Dominant Weight Normalization (NBD). The accuracy results obtained that the MFCC method with the selection of the 9th frame had a higher accuracy rate of 86% compared to other frames. The MFCC without feature selection had an average of 60%. The conclusion was that selecting the right features in the 9th frame impacted the accuracy of the voice of shahada recitation.