Acoustic Analysis of Maqam Saba (Arabic Musical Mode): Quantitative Detection of Microtones Using Python

This research presents a digital acoustic analysis of Maqam Saba using the Python programming language to quantitatively detect its microtonal characteristics and overcome the technical biases inherent in traditional Western-centric signal processing systems. The methodology involves extracting four key features from a raw audio sample: Waveform, Mel Spectrogram, Mel-Frequency Cepstral Coefficients (MFCCs), and Root Mean Square (RMS) energy. The results demonstrate the analytical power of the computational framework in identifying a stable frequency foundation at 65 Hz and pinpointing the precise spectral deviations that define the Maqam. The data confirms that the analyzed sample is a high-intensity rhythmic musical piece with a continuously evolving spectral fingerprint. The study concludes that integrating signal processing with modern programming environments provides an objective, unbiased tool for documenting and accurately measuring Arabic musical heritage, effectively bridging the gap between subjective auditory perception and physical tonal description.