Perbandingan Implementasi Metode Mel Frequency Cepstral Coefficient Dengan Metode Linear Predictive Coding Untuk Identifikasi Suara Hukum Bacaan Gunnah Dan Ikhfa’ Pada Surat Al-Kahfi
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Aji, Yusuf Kurnia
Aziza, Rosida Nur
Yosrita, Efy
Aziza, Rosida Nur
Yosrita, Efy
Subject
Teknik Informatika
Datestamp
2022-09-26 06:22:53
Abstract :
This research discusses the application of voice recognition to compare the implementation of accuration comparison using MFCC and LPC method. The MFCC method is a method used to process feature extraction, which is a process to convert voice signals to several parameters, while LPC method is one of the parametric method used to represent signals. This research compares the process stages of each method starting from the process of pre-emphasis, frame blocking, windowing and the difference of these two methods is the FFT process and autocorrelation. The sound object being compared is the sound of ikhfa and gunnah tajweed in Q.S. Al-Kahf (1-5). The recorded voice data is classified using a neural network perceptron by dividing the data for each recitation with a value ranging from 0 to 1 and then divided into 6 tajweed classes. This research uses the Matlab 2019A as a tool to make the comparison application of MFCC and LPC. In the process of testing voice comparison application, it uses the Mean Absolute Percentage Error (MAPE) and carries out five test data for each 6 tajwid classes. The MAPE results of testing in this study were 17.35 for the MFCC method and the MAPE results for the LPC method were 17.64.
This research discusses the application of voice recognition to compare the implementation of accuration comparison using MFCC and LPC method. The MFCC method is a method used to process feature extraction, which is a process to convert voice signals to several parameters, while LPC method is one of the parametric method used to represent signals. This research compares the process stages of each method starting from the process of pre-emphasis, frame blocking, windowing and the difference of these two methods is the FFT process and autocorrelation. The sound object being compared is the sound of ikhfa and gunnah tajweed in Q.S. Al-Kahf (1-5). The recorded voice data is classified using a neural network perceptron by dividing the data for each recitation with a value ranging from 0 to 1 and then divided into 6 tajweed classes. This research uses the Matlab 2019A as a tool to make the comparison application of MFCC and LPC. In the process of testing voice comparison application, it uses the Mean Absolute Percentage Error (MAPE) and carries out five test data for each 6 tajwid classes. The MAPE results of testing in this study were 17.35 for the MFCC method and the MAPE results for the LPC method were 17.64.
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