Almomani, A, Nahar, Khalid M.O. and Abual-Rub, Mohammed Said (2023) Automatic Recognition of Arabic Poetry Meter Using Machine Learning, Template Matching, and Deep Learning. In: 2023 3rd International Conference on Computing and Information Technology (ICCIT), 13-14 September 2023, Tabuk, Saudi Arabia.
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This paper discusses three methods to identify the Arabic poetry meter; namely: Machine Learning (ML), Template Matching (TM), and Deep Learning (DL). The Mel Frequency Cepstral Coefficients (MFCC) features were extracted. The MFCC features are used to represent the spectrogram and frequency domains, then they are used with ML, TM, and DL models. In TM, the following methods /algorithms are used: the Structural Similarity Index Measure (SSIM) and Oriented FAST, rotated BRIEF (ORB), and a type of Convolutional Neural Network (CNN)- which is the VGG16 deep learner. The final results show that ML outperforms other methods (TM, and DL) with an overall accuracy of 92.2%. The results are promising for auto recognition of the Arabic poetry meter -which is important for Arabic poetry learning.
Affiliation: | Skyline University College |
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SUC Author(s): | Almomani, A ORCID: https://orcid.org/0000-0002-8808-6114 |
All Author(s): | Almomani, A, Nahar, Khalid M.O. and Abual-Rub, Mohammed Said |
Item Type: | Conference or Workshop Item (Paper) |
Uncontrolled Keywords: | Meters, Deep learning, Machine learning algorithms, Feature extraction, Convolutional neural networks, Velocity measurement, Mel frequency cepstral coefficient |
Subjects: | B Information Technology > BL Machine Learning B Information Technology > BR Deep Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 15 Feb 2024 08:25 |
Last Modified: | 15 Feb 2024 08:25 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/842 |
Publisher URL: | https://doi.org/10.1109/ICCIT58132.2023.10273919 |
Publisher OA policy: | |
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