SECTION-LEVEL MODELING OF MUSICAL AUDIO FOR LINKING PERFORMANCES TO SCORES IN TURKISH MAKAMMUSIC

Section linking aims at relating structural units in the notation of a piece of music to their occurrences in a performance of the piece. In this paper, we address this task by presenting a score-informed hierarchical Hidden Markov Model (HHMM) for modeling musical audio signals on the temporal level of sections present in a compo-sition, where the main idea is to explicitly model the long range and hierarchical structure of music signals. So far, approaches based on HHMM or similar methods were mainly developed for a note-to-note alignment, i.e. an alignment based on shorter temporal units than sections. Such approaches, however, are conceptually problem-atic when the performances differ substantially from the reference score due to interpretation and improvisation, a very common phe-nomenon, for instance, in Turkish makam music. In addition to hav-ing low computational complexity compared to note-to-note align-ment and achieving a transparent and elegant model, the experimen-tal results show that our method outperforms a previously presented approach on a Turkish makam music corpus. Index Terms — Audio-to-score alignment, Section linking, Hi-erarchical hidden Markov models, Turkish makam musi