Musical note extraction algorithm - Chong, Lee Yee (2016)
This report investigates on the transcription of polyphonic musical signal. Onset time is determined to divide the whole signal into segments for better recognition ofnotes. Constant QTransform (CQT) is then used to transform each segment from time domain to frequency domain. A threshold level is set to find the fundamental and harmonic frequency peaks. The kcq values that correspond to these peaks are generated and stored for latter recognition process. Recognition methods such as Top-down Analysis and Tone Model method are implemented to detect the possible note. Piano music, such as Twinkle Twinkle Little Star and Skip to my Lou are tested. The recognition of onemember score and two-member score give 100% successful detection. These results are presented in term of the identified notes and their duration and loudness. Further investigation on other instruments, such as flute and guitar are tested. Polyphonic music played using both guitar and piano is analyzed too. Perfect detection once again confirms the accuracy of this recognition algorithm.