An investigation of prior knowledge in Automatic Music Transcription systems

Automatic transcription of music is a long-studied research field with many operational systems available commercially. In this paper, a generic transcription system able to host various prior knowledge parameters has been developed, followed by an in-depth investigation of their impact on music transcription. Explicit links between musical knowledge and algorithmic formalism have been made. Musical knowledge covers classes of timbre, musicology, and playing style of an instrument repertoire. An evaluation sound corpus gathering musical pieces played by human performers from three different instrument repertoires, namely, classical piano, steel-string acoustic guitar, and the marovany zither from Madagascar, has been developed. The different components of musical knowledge have been successively incorporated in a complete transcription system, consisting mainly of a Probabilistic Latent Component Analysis algorithm post-processed with a Hidden Markov Model, and their impact on transcription results have been comparatively evaluated.