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  • Eco-Structural Data Forms: Classification Of Data Analysis Using Perceived Design Affordances For Musical Outcomes

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    Author(s)
    Opie, Timothy
    Brown, Andrew
    Griffith University Author(s)
    Brown, Andrew R.
    Year published
    2009
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    Abstract
    This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional ...
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    This paper explores a method of comparative analysis and classification of data through perceived design affordances. Included is discussion about the musical potential of data forms that are derived through eco-structural analysis of musical features inherent in audio recordings of natural sounds. A system of classification of these forms is proposed based on their structural contours. The classifications include four primitive types; steady, iterative, unstable and impulse. The classification extends previous taxonomies used to describe the gestural morphology of sound. The methods presented are used to provide compositional support for eco-structuralism.
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    Conference Title
    Improvise: The Australasian Computer Music Conference 2009
    Publisher URI
    http://conference.acma.asn.au/ocs/index.php/acmc/acmc09
    Copyright Statement
    © The Author(s) 2009. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference's website or contact the authors.
    Subject
    Performing Arts and Creative Writing not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/40294
    Collection
    • Conference outputs

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