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Mel-Frequency-based Feature Analysis of Audio Signals in the Context of Holy Quran Recitation

  • Research Article--Electrical Engineering
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Abstract

Different sounds have various effects on human health, and by introducing the ones that are therapeutic, a healing environment can be created. This paper describes the process to train and test a machine learning algorithm to describe and explore the therapeutic nature of Quranic verse. Using a dataset containing four emotional states namely happy, sad, angry, and relaxed, we trained a model and classified different recitations of the Quran into one of these states. This paper proposes the use of Mel-frequency cepstral coefficients (MFCC) to extract features from Quranic audio and classify it with respect to a known dataset. Based on the experiments conducted on Quranic verses, we summarize our results.

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  1. Department of Electrical Engineering, School of Engineering, University of Management and Technology, C-II Johar Town, Lahore, Punjab, 54770, Pakistan

    Muhammad Faizan, Muhammad Sameer Arif, Jawwad Nasar Chattha & Faran Awais Butt

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  1. Muhammad Faizan
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  2. Muhammad Sameer Arif
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  3. Jawwad Nasar Chattha
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  4. Faran Awais Butt
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Correspondence to Muhammad Sameer Arif.

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Faizan, M., Arif, M.S., Chattha, J.N. et al. Mel-Frequency-based Feature Analysis of Audio Signals in the Context of Holy Quran Recitation. Arab J Sci Eng 49, 6971–6979 (2024). https://doi.org/10.1007/s13369-023-08555-5

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