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

Child L, Ford N. Towards an audio attribute framework for understanding the perception of reverberant spaces elicitation and clustering methods based on participant expectation. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020.pp. 10467:1-10. Available from: https://aes.org/publications/elibrary-page/?id=21144

@inproceedings{Child2020_21144,
  author = {Child, Luke and Ford, Natanya},
  title = {{Towards an audio attribute framework for understanding the perception of reverberant spaces elicitation and clustering methods based on participant expectation}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  pages = {10467:1-10},
  url = {https://aes.org/publications/elibrary-page/?id=21144},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society}
}

TY  - CPAPER
TI  - Towards an audio attribute framework for understanding the perception of reverberant spaces elicitation and clustering methods based on participant expectation
AU  - Child, Luke
AU  - Ford, Natanya
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
SP  - 1
EP  - 10
M1  - Paper 10467
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21144
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -