Distance Mapping for Corpus-Based Concatenative SynthesisReport as inadecuate




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1 Equipe Interactions musicales temps-réel STMS - Sciences et Technologies de la Musique et du Son

Abstract : 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.

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Keywords : audio descriptors corpus-based synthesis audio mosaicing databases content-based retrieval unit selection concatenative synthesis Constraints

Mots-clés : Informatique musicale





Author: Diemo Schwarz -

Source: https://hal.archives-ouvertes.fr/



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