Sound Object Classification for Symbolic Audio Mosaicing: A Proof-of-Concept

TitleSound Object Classification for Symbolic Audio Mosaicing: A Proof-of-Concept
Publication TypeConference Paper
Year of Publication2009
Conference NameSound and Music Computing Conference
AuthorsJaner, J., Haro G. G., Roma G., Fujishima T., & Kojima N.
Pagination297-302
Conference Start Date23/07/2009
Conference LocationPorto, Portugal.
ISBN Number978-989-95577-6-5
AbstractSample-based music composition often involves the task of manually searching appropriate samples from existing audio. Audio mosaicing can be regarded as a way to automatize this process by specifying the desired audio attributes, so that sound snippets that match these attributes are concatenated in a synthesis engine. These attributes are typically derived from a target audio sequence, which might limit the musical control of the user.
In our approach, we replace the target audio sequence by a symbolic sequence constructed with pre-defined sound object categories. These sound objects are extracted by means of automatic classification  techniques. Three steps are involved in the sound object extraction process: supervised training, automatic classification and user-assisted selection. Two sound object categories are considered: percussive and noisy. We present an analysis/synthesis framework, where the user explores first a song collection using symbolic concepts to create a set of sound objects. Then, the selected sound objects are used in a performance environment based on a loop-sequencer paradigm.
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