There are two approaches to Qur’an recitation, namely talaqqi and qira'ati. Both approaches use the science of recitation containing knowledge of the rules and procedures for reading the Qur'an properly. Talaqqi requires the teacher and students to sit facing each other while qira'ati is the recitation of the Qur'an with rhythms and tones. Many studies have developed an automatic speech recognition system for Qur’an recitation to help the learning process. Feature extraction model using Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Code (LPC). The MFCC method has an accuracy of 50% to 60% while the accuracy of Linear Predictive Code (LPC) is only 45% to 50%, so the non-linear MFCC method has higher accuracy than the linear approach method. The cepstral coefficient feature that is used starts from 0 to 23 or 24 cepstral coefficients. Meanwhile, the frame taken consists of 0 to 10 frames or eleven frames. Voting for 300 recorded voice samples was tested against 200 voice recordings, both male and female voices. The frequency used was 44.100 kHz stereo 16 bit. This study aims to obtain good accuracy by selecting the right feature on the cepstral coefficient using MFCC feature extraction and matching accuracy through the selection of the cepstral coefficient feature with Dominant Weight Normalization (NBD) at TPA Nurul Huda Plus Purbayan. Accuracy results showed that the MFCC method with the selection of the 23rd cepstral coefficient has a higher accuracy rate of 90.2% compared to the others. It can be concluded that the selection of the right features on the 23rd cepstral coefficient affects the accuracy of the voice of Qur’an recitation.
The Implementation Of Mfcc Feature Extraction And Selection of Cepstral Coefficient for Qur’an Recitation in TPA (Qur’an Learning Center) Nurul Huda Plus Purbayan
