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

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.