In many applications of sound transformation, such as sound design, mixing, mastering, and composition the user interactively searches for appropriate parameters. However, automatic applications of sound transformation, such as mosaicing, may require choosing parameters without user intervention. When the target can be specified by its synthesis context, or by example (from features of the example), “adaptive effects” can provide such control. But there exist few general strategies for building adaptive effects from arbitrary sets of transformations and descriptor targets. In this study, we decouple the usually direct link between analysis and transformation in adaptive effects, attempting to include more diverse transformations and descriptors in adaptive transformation, if at the cost of additional complexity or difficulty. We build an analytic model of a deliberately simple transformation-descriptor (TD) domain, and show some preliminary results.

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We propose a strategy for integrating descriptor-driven transformation into mosaicing sound synthesis, in which samples are selected by taking into account potential distances in the transformed space. Target descriptors consisting of chroma, mel-spaced filter banks, and energy are modeled with respect to windowed bandlimited resampling and mel-spaced filters, and later corrected with gain. These transformations, however simple, allow some adaptation of textural sound material to musical contexts.

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