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dc.contributor.authorSteven Harforden_US
dc.contributor.editorHolger H. Hoosen_US
dc.contributor.editorDavid Bainbridgeen_US
dc.date.accessioned2004-10-21T04:26:35Z
dc.date.available2004-10-21T04:26:35Z
dc.date.issued2003-10-26en_US
dc.identifier.isbn0-9746194-0-Xen_US
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/38
dc.description.abstractWe introduce a neural network, known as SONNET-MAP, capable of automatic segmentation, learning and retrieval of melodies. SONNET-MAP is a synthesis of Nigrin’s SONNET (Self-Organizing Neural NETwork) architecture and an associative map derived from Carpenter, Grossberg and Reynolds’ ARTMAP. SONNET-MAP automatically segments a melody based on pitch and rhythmic grouping cues. Separate SONNET modules represent the pitch and rhythm dimensions of each segmented phrase independently, with two associative maps fusing these representations at the phrase level. Further SONNET modules aggregate these phrases forming a hierarchical memory structure that encompasses the entire melody. In addition, melodic queries may be used to retrieve any encoded melody. As far as we are aware, SONNET-MAP is the first self-organizing neural network architecture capable of automatically segmenting and retrieving melodies based on both pitch and rhythm.en_US
dc.format.extent36326 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherJohns Hopkins Universityen_US
dc.subjectIR Systems and Algorithmsen_US
dc.subjectPerception and Cognitionen_US
dc.titleAutomatic Segmentation, Learning and Retrieval of Melodies Using A Self-Organizing Neural Networken_US
dc.typeArticleen_US


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