| Résumé |
In the most common approach to corpus-based concatenative synthesis, the unit selection takes places as a content-based similarity match based on a weighted Euclidean distance between the audio descriptors of the database units, and the synthesis target. While the simplicity of this method explains the relative success of CBCS for interactive descriptor-based granular synthesis — especially when combined with a graphical interface — and audio mosaicing, and still allows to express categorical matches, certain desirable constraints can not be formulated, such as disallowing repetition of units, matching a disjunction of descriptor ranges, or asymmetric distances. We therefore propose a new method of mapping the individual signed descriptor distances by a warping function that can express these criteria, while still being amenable to efficient multi-dimensional search indices like the kD-tree, for which we define the preconditions and cases of applicability. |
