Learning preferences and soundscapes for augmented hearing

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

Standard

Learning preferences and soundscapes for augmented hearing. / Korzepa, Maciej Jan; Johansen, Benjamin; Petersen, Michael Kai; Larsen, Jan; Larsen, Jakob Eg; Pontoppidan, Niels Henrik.

Proceedings of Intelligent User Interfaces. Association for Computing Machinery, 2018.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

Harvard

Korzepa, MJ, Johansen, B, Petersen, MK, Larsen, J, Larsen, JE & Pontoppidan, NH 2018, Learning preferences and soundscapes for augmented hearing. in Proceedings of Intelligent User Interfaces. Association for Computing Machinery, 23rd Intetnational Conference on Intelligent User Interface, Tokyo, Japan, 07/03/2018.

APA

Korzepa, M. J., Johansen, B., Petersen, M. K., Larsen, J., Larsen, J. E., & Pontoppidan, N. H. (2018). Learning preferences and soundscapes for augmented hearing. In Proceedings of Intelligent User Interfaces Association for Computing Machinery.

CBE

Korzepa MJ, Johansen B, Petersen MK, Larsen J, Larsen JE, Pontoppidan NH. 2018. Learning preferences and soundscapes for augmented hearing. In Proceedings of Intelligent User Interfaces. Association for Computing Machinery.

MLA

Korzepa, Maciej Jan et al. "Learning preferences and soundscapes for augmented hearing". Proceedings of Intelligent User Interfaces. Association for Computing Machinery. 2018.

Vancouver

Korzepa MJ, Johansen B, Petersen MK, Larsen J, Larsen JE, Pontoppidan NH. Learning preferences and soundscapes for augmented hearing. In Proceedings of Intelligent User Interfaces. Association for Computing Machinery. 2018

Author

Korzepa, Maciej Jan ; Johansen, Benjamin ; Petersen, Michael Kai ; Larsen, Jan ; Larsen, Jakob Eg ; Pontoppidan, Niels Henrik. / Learning preferences and soundscapes for augmented hearing. Proceedings of Intelligent User Interfaces. Association for Computing Machinery, 2018.

Bibtex

@inproceedings{4b32231d7fd44bb8b3035e28cf138d5b,
title = "Learning preferences and soundscapes for augmented hearing",
abstract = "Despite the technological advancement of modern hearing aids (HA), many users abandon their devices due to lack of personalization. This is caused by the limited hearing health care resources resulting in users getting only a default ’one size fits all’ setting. However, the emergence of smartphoneconnected HA enables the devices to learn behavioral patterns inferred from user interactions and corresponding soundscape. Such data could enable adaptation of settings to individualuser needs dependent on the acoustic environments. In our pilot study, we look into how two test subjects adjust their HA settings, and identify main behavioral patterns that help to explain their needs and preferences in different auditory conditions. Subsequently, we sketch out possibilities and challenges of learning contextual preferences of HA users. Finally, we consider how to encompass these aspects in the design of intelligent interfaces enabling smartphone-connectedHA to continuously adapt their settings to context-dependent user needs.",
author = "Korzepa, {Maciej Jan} and Benjamin Johansen and Petersen, {Michael Kai} and Jan Larsen and Larsen, {Jakob Eg} and Pontoppidan, {Niels Henrik}",
year = "2018",
language = "English",
booktitle = "Proceedings of Intelligent User Interfaces",
publisher = "Association for Computing Machinery",

}

RIS

TY - GEN

T1 - Learning preferences and soundscapes for augmented hearing

AU - Korzepa, Maciej Jan

AU - Johansen, Benjamin

AU - Petersen, Michael Kai

AU - Larsen, Jan

AU - Larsen, Jakob Eg

AU - Pontoppidan, Niels Henrik

PY - 2018

Y1 - 2018

N2 - Despite the technological advancement of modern hearing aids (HA), many users abandon their devices due to lack of personalization. This is caused by the limited hearing health care resources resulting in users getting only a default ’one size fits all’ setting. However, the emergence of smartphoneconnected HA enables the devices to learn behavioral patterns inferred from user interactions and corresponding soundscape. Such data could enable adaptation of settings to individualuser needs dependent on the acoustic environments. In our pilot study, we look into how two test subjects adjust their HA settings, and identify main behavioral patterns that help to explain their needs and preferences in different auditory conditions. Subsequently, we sketch out possibilities and challenges of learning contextual preferences of HA users. Finally, we consider how to encompass these aspects in the design of intelligent interfaces enabling smartphone-connectedHA to continuously adapt their settings to context-dependent user needs.

AB - Despite the technological advancement of modern hearing aids (HA), many users abandon their devices due to lack of personalization. This is caused by the limited hearing health care resources resulting in users getting only a default ’one size fits all’ setting. However, the emergence of smartphoneconnected HA enables the devices to learn behavioral patterns inferred from user interactions and corresponding soundscape. Such data could enable adaptation of settings to individualuser needs dependent on the acoustic environments. In our pilot study, we look into how two test subjects adjust their HA settings, and identify main behavioral patterns that help to explain their needs and preferences in different auditory conditions. Subsequently, we sketch out possibilities and challenges of learning contextual preferences of HA users. Finally, we consider how to encompass these aspects in the design of intelligent interfaces enabling smartphone-connectedHA to continuously adapt their settings to context-dependent user needs.

M3 - Article in proceedings

BT - Proceedings of Intelligent User Interfaces

PB - Association for Computing Machinery

ER -