Aris Tjahyanto, Mauridhi Hery Purnomo, Yoyon Kusnendar Suprapto, Diah Puspito Wulandari : FFT-based features selection for Javanese music note and instrument identification using Support Vector Machines
Dr. Ir. Aris Tjahyanto M.Kom.
Prof.Dr.Ir. Mauridhi Hery Purnomo M.Eng.
Dr. Ir. Yoyon Kusnendar Suprapto MSc.
Diah Puspito Wulandari S.T., M.Sc.
IEEE Int. Conf. on Computer Sci. and Automation Eng (CSAE)
support vector machine gamelan music transcription FFT spectral features
Most automatic music transcription research is related with Western music, and still less for the Javanese
gamelan music. In this paper, we proposed a method for the features extraction, selection, and identification of gamelan note and the proper instrument. It was an approach based on Fast Fourier Transform (FFT), and support vector machines (SVMs) for note and instrument identification. We selected four spectral features (spectral centroid, two spectral rolloff, and fundamental frequency) as input for SVM. Experimental results show that fundamental frequency, spectral centroid, and spectral rolloff can be used to distinguish gamelan instrument with accuracy or recognition rate more than 95.