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- W49287718 abstract "In timbre remapping applications such as concatenative synthesis, an audio signal is used as a template, and a mapping process derives control data for some audio synthesis algorithm such that it produces a new audio signal approximating the perceived trajectory of the original sound. Timbre is a multidimensional attribute with interactions between dimensions, and the control and synthesised signals typically represent sounds with dierent timbral ranges, so it is non-trivial to design a search process which makes best use of the timbral variety available in the synthesiser. We rst discuss our preliminary work applying standard machine-learning techniques for this purpose (PCA, self-organising maps), and the reasons they were not satisfactory. We then describe a novel regression-tree technique which learns associations between unlabelled multidimensional timbre distributions." @default.
- W49287718 created "2016-06-24" @default.
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- W49287718 date "2010-09-30" @default.
- W49287718 modified "2023-09-27" @default.
- W49287718 title "Cross-associating unlabelled timbre distributions to create expressive musical mappings" @default.
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