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dc.contributor.authorAbdallah, Sen_US
dc.contributor.authorPlumbley, Men_US
dc.contributor.authorGeometric Science of Informationen_US
dc.date.accessioned2014-06-12T10:46:32Z
dc.date.issued2013-08-28en_US
dc.identifier.isbn9783642400193en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/5919
dc.descriptionThis is the author's accepted manuscript of this article. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40020-9.
dc.descriptionLecture Notes in Computer Science
dc.descriptionLecture Notes in Computer Scienceen_US
dc.description.abstractWe describe an information-theoretic approach to the analysis of sequential data, which emphasises the predictive aspects of perception, and the dynamic process of forming and modifying expectations about an unfolding stream of data, characterising these using a set of process information measures. After reviewing the theoretical foundations and the definition of the predictive information rate, we describe how this can be computed for Gaussian processes, including how the approach can be adpated to non-stationary processes, using an online Bayesian spectral estimation method to compute the Bayesian surprise. We finish with a sample analysis of a recording of Steve Reich’s Drummingen_US
dc.format.extent650 - 657en_US
dc.publisherSpringer Berlin Heidelbergen_US
dc.subjectalgorithm analysisen_US
dc.subjectpattern recognitionen_US
dc.subjectartificial intelligenceen_US
dc.subjectimage processingen_US
dc.titlePredictive information in Gaussian processes with application to music analysisen_US
dc.typeConference Proceeding
dc.identifier.doi10.1007/978364240020972en_US
pubs.issueXVIIIen_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.publisher-urlhttp://link.springer.com/chapter/10.1007/978-3-642-40020-9_72en_US
pubs.volume8085 LNCSen_US


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