Automatic transcription of monophonic piano music

Abstract

Automatic music transcription refers to the process of transforming an acoustic musical signal into a written symbolic representation, e.g. a score. This process consists of extracting the parameters of note events, for instance pitches, onset times, and durations, from a raw acoustic signal. We have developed a novel algorithm for transcription of monophonic piano music, which addresses the challenges of pitch and note sequence detection in two stages: (1) The K-Nearest Neighbor (KNN) classification method is employed to identify K pitch candidates, based on spectral information, for each note event. (2) The most likely note sequence is determined by running a best-first tree search over the note candidates based on both spectral information and note transition probability distributions. The proposed two-step approach provides the performance gain achieved by incorporating note transition probabilities while maintaining significantly lower computational complexity than existing support vector machine and...