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Learning musical pitch structures with hierarchical hidden Markov models

Abstract : In this paper we attempt to demonstrate the strengths of Hierarchical Hidden Markov Models (HHMMs) in the representation and modelling of musical structures. We show how relatively simple HHMMs, containing a minimum of expert knowledge, use their advantage of having multiple layers to perform well on tasks where flat Hidden Markov Models (HMMs) struggle. The examples in this paper show a HHMM's performance at extracting higherlevel musical properties through the construction of simple pitch sequences, correctly representing the data set on which it was trained.
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Submitted on : Tuesday, January 19, 2021 - 11:44:08 AM
Last modification on : Thursday, July 14, 2022 - 3:53:30 AM
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  • HAL Id : hal-03114882, version 1


Michèle Weiland, Alan Smaill, Peter Nelson. Learning musical pitch structures with hierarchical hidden Markov models. Journées d'Informatique Musicale 2005, Association Française d'Informatique Musicale; Centre de recherche en Informatique et Création Musicale, Jun 2005, Saint-Denis, France. ⟨hal-03114882⟩



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