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Unsupervised statistical learning underpins computational, behavioural, and neural manifestations of musical expectation

, , , , and . NeuroImage, (Dec 11, 2009)
DOI: 10.1016/j.neuroimage.2009.12.019

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How Many Beans Make Five? The Consensus Problem in Music-Genre Classification and a New Evaluation Method for Single-Genre Categorisation Systems., , and . ISMIR, page 73-76. Austrian Computer Society, (2007)ATEPP: A Dataset of Automatically Transcribed Expressive Piano Performance., , , , , and . ISMIR, page 446-453. (2022)Synthesis and Transformation of Logic Programs in the Whelk Proof Development System.. JICSLP, page 351-365. MIT Press, (1992)Visualisation of live code., , , and . EVA, BCS, (2010)Meta-Evaluating Quantitative Internal Evaluation: A Practical Approach for Developers., , , , and . ICCC, page 213-217. Association for Computational Creativity (ACC), (2021)Representing Trees with Constraints., , and . Computational Logic, volume 1861 of Lecture Notes in Computer Science, page 315-325. Springer, (2000)A Dynamic Systems Approach to Modelling Human-Machine Rhythm Interaction., , , and . CoRR, (2024)Engagement-Reflection in Software Construction., and . ICCC, page 321-325. Association for Computational Creativity (ACC), (2019)Conceptualising Computational Creativity: Towards automated historiography of a research field., , , and . ICCC, page 288-295. Association for Computational Creativity (ACC), (2018)Music Representation - Between the Musician and the Computer., , and . Music Education: An Artificial Intelligence Approach, page 108-119. Springer, (1993)