Using Machine Learning to Mitigate Reverberation Effects in Cochlear Implants

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 DESCRIPTION (provided by applicant): Cochlear implants (CIs) provide hearing for over 200,000 recipients worldwide {NIDCD, 2011 #637}. These devices successfully provide high levels of speech understanding in quiet listening conditions; however, more challenging conditions degrade speech comprehension for CI recipients to a much greater degree than for normal hearing listeners {Kokkinakis, 2011 #673;Nelson, 2003 #302}. CI listeners are especially affected by reverberant conditions with even a small level of reverberation degrading comprehension to a greater degree than a large amount of steady-state noise {Hazrati, 2012 #674}. Thus, a method that mitigates the effects of reverberation has the potential to greatly improve the quality of life for CI users. Previous attempts to solve the problem of speech in reverberation for cochlear implants have not been able to be implemented in real time. Our preliminary results suggest that successful mitigation of overlap masking can result in a substantial improvement in speech recognition even if self-masking is not mitigated and we have devised an approach that can be implemented in real time. In the proposed effort, we will first improve the classifier to detect reverberation based on our successful preliminary efforts. Next we will assess the mitigation algorithm, first in normal hearing listeners and then in listeners with cochlear implants. Finally, we will implement the algorithm in real time and again test it.