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Since noteworthy events happen only occasionally in any data, it is imperative for
smart sensors to learn the norms in data so that an appropriate action can be taken
at the occurrence of an abnormal or noteworthy event. The aim of this project is to
develop algorithms that can learn the norm in terms of a hierarchy of meaningful features
from data in an unsupervised and online manner. The application testbed is the problem
of automatically tuning cochlear implants (CIs) of patients with severe-to-profound
hearing loss by continuously monitoring their speech output.
Hearing loss is the most common birth defect in the U.S. with slightly over 15,000
new pediatric cases each year and societal losses amounting to $4.6 billion over a
lifetime. The working hypothesis is that deficiencies in hearing for people with significant
hearing loss are reflected in their speech production. This project will develop and
use unsupervised, online, and biologically plausible machine learning algorithms to
learn feature hierarchies from the speech output data of severely-to-profoundly hearing-impaired
patients. The learned feature hierarchy from the speech of a patient will be compared
to those learned from the speech of a comparable normal hearing population. Deficiencies
in the patient's hearing will be ascertained by identifying the missing or distorted
features. Algorithms will be developed to map this information into the signal processing
strategies used in CIs to enhance the audibility of speech.
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