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Music genre classification based on dynamical models

Garcia-Duran, Alberto; Arenas-Garcia, Jeronimo; Garcia-Garcia, Dario; Parrado-Hernandez, Emilio


This paper studies several alternatives to extract dynamical features from hidden Markov Models (HMMs) that are meaningful for music genre supervised classification. Songs are modelled using a three scale approach: a first stage of short term (milliseconds) features, followed by two layers of dynamical models: a multivariate AR that provides mid term (seconds) features for each song followed by an HMM stage that captures long term (song) features shared among similar songs. We study from an...[Show more]

CollectionsANU Research Publications
Date published: 2012
Type: Conference paper
Source: ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods


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