Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform

(1) * Yovinia Carmeneja Hoar Siki Mail (Universitas Katolik Widya Mandira, Kupang, Indonesia)
(2) Natalia Magdalena Rafu Mamulak Mail (Universitas Katolik Widya Mandira, Kupang, Indonesia)
*corresponding author


Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.


gong's musical signals; time-frequency analysis; STFT; OSTFT; CWT




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M. H. Budhiantho and G. Dewantoro, “Javanese gong wave signals,” in Proceedings of Meetings on Acoustics, 2014, p. 035003.

M. H. W. Budhiantho and G. Dewantoro, “Homomorphic filtering for extracting Javanese Gong wave signals,” in 2014 8th International Conference on Telecommunication Systems Services and Applications (TSSA), 2014, pp. 1–6.

M. H. W. Budhiantho and G. Dewantoro, “The spectral and temporal description of Javanese Gong Kempul,” in 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), 2013, pp. 300–304.

A. P. Klapuri, A. J. Eronen, and J. T. Astola, “Analysis of the meter of acoustic musical signals,” IEEE Trans. Audio Speech Lang. Process., vol. 14, no. 1, pp. 342–355, Jan. 2006.

K. Hastuti and K. Mustafa, “A method for automatic gamelan music composition,” Int. J. Adv. Intell. Inform., vol. 2, no. 1, pp. 26–37, 2016.

L. Fitria, Y. K. Suprapto, and M. H. Purnomo, “Music transcription of Javanese Gamelan using Short Time Fourier Transform (STFT),” in 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA), 2015, pp. 279–284.

D. P. Wulandari, Y. K. Suprapto, and M. H. Purnomo, “Gamelan music onset detection using Elman Network,” in 2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, 2012, pp. 91–96.

T. P. Tomo, G. Enriquez, and S. Hashimoto, “Indonesian puppet theater robot with gamelan music emotion recognition,” in 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2015, pp. 1177–1182.

H. Katayose, M. Imai, and S. Inokuchi, “Sentiment extraction in music,” in [1988 Proceedings] 9th International Conference on Pattern Recognition, 1988, pp. 1083–1087 vol.2.

M. Muller, D. P. W. Ellis, A. Klapuri, and G. Richard, “Signal Processing for Music Analysis,” IEEE J. Sel. Top. Signal Process., vol. 5, no. 6, pp. 1088–1110, Oct. 2011.

M. N. Latief, F. L. Gaol, and B. H. Iswanto, “Analysis and Identification Gamelan Bonang Sound Spectrum,” in 2010 Second International Conference on Computational Intelligence, Modelling and Simulation, 2010, pp. 335–338.

J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, and M. B. Sandler, “A Tutorial on Onset Detection in Music Signals,” IEEE Trans. Speech Audio Process., vol. 13, no. 5, pp. 1035–1047, Sep. 2005.

A. Tjahyanto, D. P. Wulandari, and Y. K. Suprapto, “Time-Frequency Analysis of Gamelan Sound Signals using Continuous Wavelet Transform,” in Seminar on Intelligent Technology and Its Applications, 2014.

P. De Gersem, B. De Moor, and M. Moonen, “Applications of the continuous wavelet transform in the processing of musical signals,” in Proceedings of 13th International Conference on Digital Signal Processing, 1997, vol. 2, pp. 563–566 vol.2.

A. Paradzinets, H. Harb, and L. Chen, “Use of continuous wavelet-like transform in automated music transcription,” in Signal Processing Conference, 2006 14th European, 2006, pp. 1–4.

Y. Ge and D. G. Daut, “Bit error rate analysis of digital communications signal demodulation using wavelet denoising,” in 34th IEEE Sarnoff Symposium, 2011, pp. 1–6.

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