structure-hmm · orio2003-score-following

In a pre-processing step, the system extracts some features (e.g. pitch, spectrum, amplitude) from the sound produced by the musician. [...] The model is matched with the incoming data in the follower, while the actions score represents the actions that the accompaniment has to perform at some specified positions [...]. According to Vercoe, the score follower has to fulfill three tasks: Listen-Perform-Learn. [...] HMMs can deal with the several levels of unpredictability typical of performed music and they can model complex features, without requiring pre-processing techniques that are prone to errors like any pitch detectors or midi sensors. For instance, in our approach, the whole frequency spectrum of the signal is modeled. [...] One of the advantages of a score following system based on a statistical model is that it can learn using well-known training methods.