@CONFERENCE\{IMM2013-06585,
    author       = "J. B. Nielsen and J. Nielsen",
    title        = "Efficient Individualization of Hearing Aid Processed Sound",
    year         = "2013",
    month        = "may",
    keywords     = "Individualization, Gaussian Process (GP), Bayesian, Active Learning, Expected Improvement (EI), Hearing Aid",
    booktitle    = "{IEEE} International Conference on Acoustics, Speech, and Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6585-full.html",
    abstract     = "Due to the large amount of options offered by the vast number of adjustable parameters in modern digital hearing aids, it is becoming increasingly daunting—even for a fine-tuning professional—to perform parameter fine tuning to satisfactorily meet the preference of the hearing aid user. In addition, the communication between the fine-tuning professional and the hearing aid user might muddle the task. In the present paper, an interactive system is proposed to ease and speed up fine tuning of hearing aids to suit the preference of the individual user. The system simultaneously makes the user conscious of his own preferences while the system itself learns the user’s preference. Since the learning is based on probabilistic modeling concepts, the system handles inconsistent user feedback efficiently. Experiments with hearing impaired subjects show that the system quickly discovers individual preferred hearing-aid settings which are consistent across consecutive fine-tuning sessions for each user."
}