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Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks

dc.citation.epage185en_US
dc.citation.issue1en_US
dc.citation.spage171en_US
dc.citation.volume11en_US
dc.citation.woscount10
dc.contributor.authorLiang, SFen_US
dc.contributor.authorSu, AWYen_US
dc.contributor.authorLin, CTen_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.date.accessioned2014-12-08T15:45:54Z
dc.date.available2014-12-08T15:45:54Z
dc.date.issued2000-01-01en_US
dc.description.abstractA physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings, The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN, After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.en_US
dc.identifier.doi10.1109/72.822519en_US
dc.identifier.issn1045-9227en_US
dc.identifier.journalIEEE TRANSACTIONS ON NEURAL NETWORKSen_US
dc.identifier.urihttp://dx.doi.org/10.1109/72.822519en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/30861
dc.identifier.wosnumberWOS:000085524000016
dc.language.isoen_USen_US
dc.subjectphysical modelingen_US
dc.subjectplucked string instrumentsen_US
dc.subjectscattering recurrent networksen_US
dc.titleModel-based synthesis of plucked string instruments by using a class of scattering recurrent networksen_US
dc.typeArticleen_US

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