Distance Mapping for Corpus-Based Concatenative Synthesis

Publication Type:

Conference Paper


Diemo Schwarz


SMC Conference 2011 (2011)


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 expressed, such as disallowing repetition of units, matching a disjunction of descriptor ranges, or asymmetric distances.

We therefore map the individual 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.

smc2011_submission_127.pdf207.29 KB
SMC paper: