An automatic music transcription system dedicated to the repertoires of the marovany zither

The meeting between Music information retrieval (MIR) and Ethnomusicology has given way to a new scientific discipline called Computational Ethnomusicology, which aims to adapt and develop MIR tools for the multiple corpus of ethnic music. In this paper, we are interested in developing an automatic transcription system dedicated to the marovany musical repertoires. The marovany repertoires are mostly orally transmitted, resulting from a process of memorization/transformation of original base musical motives. These motives represent an important culture patrimony, and are evolving continually under the influences of other musical practices and genres due to globalization. Our transcription system consists mainly of a Probabilistic Latent Component Analysis algorithm post-processed with Hidden Markov Model. It incorporates repertoire-specific music-related prior knowledge about timbre, music language and playing style. The impact of each knowledge component on transcription results has been comparatively evaluated. The most beneficial prior combination brought a transcription gain of 7.4% from the note-based F-measures of state-of-the-art transcription system performance.