{"pk":24616,"title":"Inferring Musical Structure - A Hybrid Approach Combining Probabilistic Models and Reinforcement Learning","subtitle":null,"abstract":"How do humans infer the structural interpretations of a piece of music from its basic elements? Since recursive elaboration is an important structural principle in several musical traditions, generative probabilistic models are a useful tool for characterizing musical interpretation as a probabilistic inference problem. However, due to the high degree of ambiguity and combinatorial complexity of even short excerpts of music, exact inference (e.g. finding the \"best\" structural interpretation of a piece) is usually not feasible. The present work proposes a hybrid approach to this problem. An explicit and interpretable probabilistic top-down model is complemented with a heuristic parser that reverses the generative process in a greedy fashion and adapts to feedback from the top-down model via deep reinforcement learning. The combination of these two models bridges the gap between explicit but slow top-down knowledge and immediate musical intuitions on various levels of musicianship.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Machine learning; Music; Perception; Bayesian modeling; Computational Modeling"}],"section":"Abstracts","is_remote":true,"remote_url":"https://escholarship.org/uc/item/5g10x1pv","frozenauthors":[{"first_name":"Christoph","middle_name":"","last_name":"Finkensiep","name_suffix":"","institution":"University of Amsterdam","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2024-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/24616/galley/17976/download/"},{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/24616/galley/21279/download/"}]}