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High-Level Semantic Metadata for the Control of Multitrack Adaptive Digital Audio Effects
Existing adaptive digital audio effects predominantly use low-level features in order to derive control data. These data do not typically correspond to high-level musicological or semantic information about the content. In order to apply audio transformations selectively on different musical events in a multitrack project, audio engineers and music producers have to resort to manual selection or annotation of the tracks in traditional audio production environments. We propose a new class of audio effects that uses high-level semantic audio features in order to obtain control data for multitrack effects. The metadata is expressed in RDF using several music and audio related Semantic Web ontologies and retrieved using the SPARQL query language.
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