Automatic Recognition of Arabic Poetry Meter Using Machine Learning, Template Matching, and Deep Learning

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.

Full text not available from this repository. (Request a copy)

Abstract

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
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:
Related URLs:

    Actions (login required)

    View Item
    View Item
    Statistics for SkyRep ePrint 842 Statistics for this ePrint Item