The musical descriptor using Fast Fourier Transform (FFT): examples in P Ramlee songs - Musib, Ahmad Faudzi; Hamdan, Sinin; Kuryati, Kipli; Sinin, Aaliyawani Ezzerin; Johari Abdullah, Johari Abdullah (University of Brunei Darussalam, 2024)
Abstract The musical descriptor can be characterize using quantitative evaluation of audio sounds. This paper evaluates the musical descriptors for P Ramlee instrumental songs utilizing the Fast Fourier Transform (FFT) from the audio sounds signal. In this study, Python programming language is used to develop a series of custom scripts to calculate the musical descriptors. The approach involved constructing musical descriptors from instrumental music through analysis and digital processing of the spectra. The signal data were first transformed using the FFT to extract frequencies and intensities, which are crucial for identifying the spectral signature of musical sounds. The FFT is particularly valuable as it uniquely represents the spectral content of any musical sound by transforming the time-domain signal into its frequency- domain components. The Affinity coefficient, A, increased from 1.43 (Jangan Tinggal Daku) to 6.67 (Dendang Perantau), 8.96 (Di Pinggiran) and 13.81 (Getaran Jiwa). The Sharpness coefficient, S for Di Pinggiran is 0.039, Getaran Jiwa is 0.040, Dendang Perantau is 0.056 and Jangan Tinggal Daku is 0.065. The Harmonicity coefficient H for Di Pinggiran showed the highest H i.e. 17.0 followed by Dendang Perantau i.e. 8.2, Jangan Tinggal Daku i.e. 7.5 and Getaran Jiwa i.e. only 5.0. Monotony coefficient M of Dendang Perantau showed the lowest M i.e. ¡0.0096 followed by Di Pinggiran i.e. ¡0.0013, Jangan Tinggal Daku i.e. 0.0198 and Getaran Jiwa i.e. 0.0638. The Mean Affinity (MA) increase from 0.1689 (Getaran Jiwa) to 0.5322 (Dendang Perantau), 0.8673 (Di Pinggiran) and 1.3877 (Jangan Tinggal Daku). The Mean Contrast (MC) increase from 0.1568 (Di Pinggiran) to 0.3160 (Dendang Perantau), 0.4430 (Jangan Tinggal Daku) and 0.4755 (Getaran Jiwa). Keywords: Affinity, Sharpness, Harmonicity, Monotony, Mean Affinity and Mean Contrast