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• W2749429919 abstract "Abstract Forecasting the future behaviour of a system using past data is an important topic. In this article we apply nonlinear time series analysis in the context of music, and present new algorithms for extending a sample of music, while maintaining characteristics similar to the original piece. By using ideas from ergodic theory, we adapt the classical prediction method of Lorenz analogues so as to take into account recurrence times, and demonstrate with examples, how the new algorithm can produce predictions with a high degree of similarity to the original sample." @default.
• W2749429919 created "2017-08-31" @default.
• W2749429919 creator A5031834766 @default.
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• W2749429919 date "2018-03-01" @default.
• W2749429919 modified "2023-09-24" @default.
• W2749429919 title "A recurrence-weighted prediction algorithm for musical analysis" @default.
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• W2749429919 doi "https://doi.org/10.1016/j.cnsns.2017.08.017" @default.
• W2749429919 hasPublicationYear "2018" @default.
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