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Vol. 9, No. 2, May 2024

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Enhancing Qur'anic Recitation Experience with CNN and MFCC Features for Emotion Identification

https://doi.org/10.22219/kinetik.v9i2.2007
Lailis Syafa'ah
Universitas Muhammadiyah Malang
Roby Prasetyono
Universitas Muhammadiyah Malang
Hariyady Hariyady
Universitas Muhammadiyah Malang

Corresponding Author(s) : Lailis Syafa'ah

lailis@umm.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 2, May 2024

Abstract

In this study, MFCC feature extraction and CNN algorithms are used to examine the identification of emotions in the murottal sounds of the Qur'an. A CNN model with labelled emotions is trained and tested, as well as data collection of Qur'anic murottal voices from a variety of readers using MFCC feature extraction to capture acoustic properties. The outcomes show that MFCC and CNN work together to significantly improve emotion identification. The CNN model attains an accuracy rate of 56 percent with the Adam optimizer (batch size 8) and a minimum of 45 percent with the RMSprop optimizer (batch size 16). Notably, accuracy is improved by using fewer emotional parameters, and the Adam optimizer is stable across a range of batch sizes. With its insightful analysis of emotional expression and user-specific recommendations, this work advances the field of emotion identification technology in the context of multitonal music.

Keywords

Emotion identification, Qur'an murottal sound, MelFrequency Cepstral Coefficients (MFCC), Convolutional Neural Network (CNN)
Syafa’ah, L., Prasetyono, R., & Hariyady, H. (2024). Enhancing Qur’anic Recitation Experience with CNN and MFCC Features for Emotion Identification. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(2), 181-192. https://doi.org/10.22219/kinetik.v9i2.2007
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