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dc.contributor.authorPage, M.P.A.
dc.date.accessioned2012-01-24T14:01:02Z
dc.date.available2012-01-24T14:01:02Z
dc.date.issued1994
dc.identifier.citationPage , M P A 1994 , ' Modelling the perception of musical sequences with self-organizing neural networks ' , Connection Science , vol. 6 , no. 2-3 , pp. 223-246 . https://doi.org/10.1080/09540099408915725
dc.identifier.issn1360-0494
dc.identifier.otherPURE: 531076
dc.identifier.otherPURE UUID: 8e15f483-54bd-418b-b9c3-a6ab95f57b7b
dc.identifier.otherBibtex: urn:0b9aa3b042f7367444653308b07afea4
dc.identifier.otherScopus: 21844515942
dc.identifier.urihttp://hdl.handle.net/2299/7713
dc.description.abstractA brief review of studies into the psychology of melody perception leads to the conclusion that melodies are represented in long-term memory as sequences of specific items, either intervals or scale notes; the latter representation is preferred. Previous connectionist models of musical-sequence learning are discussed and criticized as models of perception. The Cohen— Grossberg masking field (Cohen & Grossberg, 1987) is described and it is shown how it can be used to generate melodic expectations when incorporated within an adaptive resonance architecture. An improved formulation, the SONNET 1 network (Nigrin, 1990, 1992), is described in detail and modifications are suggested. The network is tested on its ability to learn short melodic phrases taken from a set of simple melodies, before being applied to the learning of the melodies themselves. Mechanisms are suggested for sequence recognition and sequence recall. The advantages of this approach to sequence learning are discussed.en
dc.format.extent24
dc.language.isoeng
dc.relation.ispartofConnection Science
dc.titleModelling the perception of musical sequences with self-organizing neural networksen
dc.contributor.institutionDepartment of Psychology
dc.contributor.institutionHealth & Human Sciences Research Institute
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1080/09540099408915725
rioxxterms.typeJournal Article/Review


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