Conditional Restricted Boltzmann Machines for Mono/Polyphonic Composer Identification

Research output: Unpublished contribution to conferenceUnpublished paper

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Abstract

In this paper, the effectiveness of Conditional Restricted Boltzmann Machines (CRBMs) as universal feature extractors for classifying symbolic music is investigated. An average monophonic classification accuracy of 72% was achieved when discriminating between string quartets movements of Mozart and Haydn. When the decisions of individual monophonic parts were combined using a basic voting scheme, a polyphonic classification accuracy of 96% was achieved, a substantial improvement compared to the best state-of-the-art performance to date of 80%. It was observed that the classification rate depended heavily on the exact composition of training and test set: the classifier performance deteriorated when the time of composition increased between training and test set. This supports the observation that the ”style” of a composer is not a fixed given but varies over time.
Original languageEnglish
Publication statusPublished - 6 Nov 2015
Event27th Benelux Conference on Artificial Intelligence - BNAIC 2015 - University of Hasselt, Hasselt, Belgium
Duration: 5 Nov 20156 Nov 2015

Conference

Conference27th Benelux Conference on Artificial Intelligence - BNAIC 2015
Country/TerritoryBelgium
CityHasselt
Period5/11/156/11/15

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