Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification"

Hermann T, Baier G, Stephani U, Ritter H (2008)
Bielefeld University.

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OA Sonification Example S2
OA Sonification Example S3.1
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Abstract
This paper introduces kernel regression mapping sonification (KRMS) for optimized mappings between data features and the parameter space of Parameter Mapping Sonification. Kernel regression allows to map data spaces to high-dimensional parameter spaces such that specific locations in data space with pre-determined extent are represented by selected acoustic parameter vectors. Thereby, specifically chosen correlated settings of parameters may be selected to create perceptual fingerprints, such as a particular timbre or vowel. With KRMS, the perceptual fingerprints become clearly audible and separable. Furthermore, kernel regression defines meaningful interpolations for any point in between. We present and discuss the basic approach exemplified by our previously introduced vocal EEG sonification, report new sonifications and generalize the approach towards automatic parameter mapping generators using unsupervised learning approaches. ### Sonification examples - Sonification Example [S1 (mp3, 356k)](https://pub.uni-bielefeld.de/download/2698580/2698583): formant transitions during absence EEG using a mapping of dipole x/y to the first two formants of a subtractive synthesizer. - Sonification Example [S2 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698584): formant transitions during absence EEG using a mapping of delay-embedding feature described in the paper to the first two formants. - Sonification Example Series 3: In the series from S3.1 to S3.5, it can be heard that the transitions between formants become successively sharper with decreasing bandwidth sigma: + [S3.1 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698585): sigma = 0.50 + [S3.2 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698586): sigma = 0.35 + [S3.3 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698588): sigma = 0.25 + [S3.4 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698587): sigma = 0.15 + [S3.5 (mp3, 248k)](https://pub.uni-bielefeld.de/download/2698580/2698589): sigma = 0.05 - Sonification Example [S4 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698590): same as S2, here rendered at 1/4 of real-time for better discernability of formant transitions. - Sonification Example Series 5: In the series from S5.1 to S5.5 is exactly as in S3.1-S3.5 a series with decreasing bandwidth sigma, here rendered at 1/4 of real-time for better discernability of formant transitions + [S5.1 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698591): sigma = 0.50 + [S5.2 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698593): sigma = 0.35 + [S5.3 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698592): sigma = 0.25 + [S5.4 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698594): sigma = 0.15 + [S5.5 (mp3, 496k)](https://pub.uni-bielefeld.de/download/2698580/2698595): sigma = 0.05
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This Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification" is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
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Cite this

Hermann T, Baier G, Stephani U, Ritter H. Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification". Bielefeld University; 2008.
Hermann, T., Baier, G., Stephani, U., & Ritter, H. (2008). Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification". Bielefeld University.
Hermann, T., Baier, G., Stephani, U., and Ritter, H. (2008). Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification". Bielefeld University.
Hermann, T., et al., 2008. Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification", Bielefeld University.
T. Hermann, et al., Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification", Bielefeld University, 2008.
Hermann, T., Baier, G., Stephani, U., Ritter, H.: Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification". Bielefeld University (2008).
Hermann, Thomas, Baier, Gerold, Stephani, Ulrich, and Ritter, Helge. Supplementary Material for "Kernel Regression Mapping for Vocal EEG Sonification". Bielefeld University, 2008.
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Main File(s)
File Name
File Title
Sonification Example S1
Description
Formant transitions during absence EEG using a mapping of dipole x/y to the first two formants of a subtractive synthesizer.
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OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S2
Description
Formant transitions during absence EEG using a mapping of delay-embedding feature described in the paper to the first two formants.
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S3.1
Description
It can be heard that the transitions between formants become successively sharper with decreasing bandwidth (sigma = 0.50).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S3.2
Description
It can be heard that the transitions between formants become successively sharper with decreasing bandwidth (sigma = 0.35).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S3.3
Description
It can be heard that the transitions between formants become successively sharper with decreasing bandwidth (sigma = 0.25).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S3.4
Description
It can be heard that the transitions between formants become successively sharper with decreasing bandwidth (sigma = 0.15).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S3.5
Description
It can be heard that the transitions between formants become successively sharper with decreasing bandwidth (sigma = 0.05).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S4
Description
Same as S2, here rendered at 1/4 of real-time for better discernability of formant transitions.
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S5.1
Description
Rendered at 1/4 of real-time for better discernability of formant transitions (sigma = 0.50).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S5.2
Description
Rendered at 1/4 of real-time for better discernability of formant transitions (sigma = 0.35).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S5.3
Description
Rendered at 1/4 of real-time for better discernability of formant transitions (sigma = 0.25).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S5.4
Description
Rendered at 1/4 of real-time for better discernability of formant transitions (sigma = 0.15).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z
File Name
File Title
Sonification Example S5.5
Description
Rendered at 1/4 of real-time for better discernability of formant transitions (sigma = 0.05).
Access Level
OA Open Access
Last Uploaded
2016-03-22T06:32:34Z

This data publication is cited in the following publications:
2017285
Kernel Regression Mapping for Vocal EEG Sonification
Hermann T, Baier G, Stephani U, Ritter H (2008)
In: Proceedings of the International Conference on Auditory Display (ICAD 2008). Susini P, Warusfel O (Eds);Paris, France: IRCAM.
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