Traditional music sound extraction based on spectral density model using adaptive cross-correlation for automatic transcription

Nowadays, mining of the musical ensemble attracts the interests in several aspects since the importance of archiving traditional musical performance is emphasized. However, there are very few of them which take into account the Indonesian traditional instrument called Gamelan. While western music perceives that good music is composed by stable tones, the eastern music such as gamelan has freely imposed tones in terms of resonance and tone color. Exploration of the gamelan music is very rare, so its development is far lagged to western music. The in-depth development of gamelan music is needed to bring back the greatness of this music like the one its era ((17 ^th-18 ^th century). This research initiates gamelan sound extraction for music transcription as part of traditional music analysis. In this research we introduce a new method to generate music transcription for gamelan. The spectral density model is built to extract the sound of an instrument among the others by using Adaptive Cross Correlation (ACC). The experiment demonstrates 16% note error rate for gamelan performance.