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Towards an audio attribute framework for understanding the perception of reverberant spaces elicitation and clustering methods based on participant expectation

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When used in perceptual audio evaluation, elicitation methods produce a wide variety of raw and unorganized text data. Although at first ambiguous, elicited data can be organized into themes and attributes that are intrinsic to the listener experience. This paper seeks to compare the trends found in descriptions of reverberant locations from memory, isolating key attributes and phrases present in descriptions. These attributes are then cleaned, validated, and clustered to form a series of key parent attributes that encompass the descriptions of the original attributes. Methods for the optimization of each stage are discussed, alongside applications for understanding and utilizing the attributes in future implementations of digital reverberation.

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