ACADSTAFF UGM
| Title | : | Quran Recitation Style Classification using Convolutional Neural Network and MFCC Features |
| Author | : |
MUHAMMAD ARDI PUTRA (1) Wahyono, Ph.D. (2) |
| Date | : | 0 2020 |
| Keyword | : | voice recognition,sound classification,machine learning,neural network voice recognition,sound classification,machine learning,neural network |
| Abstract | : | Reciting the holy book of Quran may be considered as an art in the religion of Islam. Basically, everyone is able to recite the book with their own style. However, there are several styles which are considered as the most famous, namely Kurdi, Ajam and Nahawand. The objective of this research is to create a CNN-based model which is able to perform classification on those different recitation styles. Several different audio features and CNN hyperparameters are experimented in order to figure out the best model configuration for this classification task. The tested audio features in this research are MFCC, Log Mel Filter Bank and SSC. On the other hand, the tuned CNN hyperparameters involve filter size, filter stride, number of filters and number of epochs. By the end of this research, it is found that the best CNN model gives 88.4% of classification accuracy towards data in test set. This result is better compared to that of MLP and LSTM model which obtains the accuracy of 85.8% and 69.1%, respectively. |
| Group of Knowledge | : | |
| Level | : | Internasional |
| Status | : |
Accepted
|
| No | Title | Action |
|---|---|---|
| 1 |
Quran Recitation Style Classification using Convolutional Neural Network and MFCC Features.pdf
Document Type : [PAK] Full Dokumen
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