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Contact Info

Laurel H. Carney, Ph.D. Department of Biomedical Engineering University of Rochester work Box 603 601 Elmwood Ave Rochester, NY 14642 office: MC 5-6418 p 585-276-3948 f 585-756-5334 Carney

Recent Publications

  • (2014 Jul). Speech enhancement for listeners with hearing loss based on a model for vowel coding in the auditory midbrain. - IEEE transactions on bio-medical engineering.
  • (2014 Jan 22). Suboptimal use of neural information in a mammalian auditory system. - The Journal of neuroscience : the official journal of the Society for Neuroscience.
  • (2014 Jan). Updated parameters and expanded simulation options for a model of the auditory periphery. - The Journal of the Acoustical Society of America.
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Graduate Student

Laurel H. Carney

  • Ph.D., University of Wisconsin, 1989
Photo of Laurel Carney
  • Professor

    • Biomedical Engineering
    • Neurobiology & Anatomy
    • Center for Navigation and Communication Sciences

Carney Lab

Research Overview

We combine neurophysiological, behavioral, and computational modeling techniques towards our goal of understanding neural mechanisms underlying the perception of complex sounds. Most of our work is focused on hearing in listeners with normal hearing ability. We are also interested in applying the results from our laboratory to the design of physiologically based signal-processing strategies to aid listeners with hearing loss.

We are currently studying two specific problems: detection of acoustic signals in background noise, and detection of fluctuations in the amplitude of sounds. These problems are of interest because they are tasks at which the healthy auditory system excels, but they are situations that can present great difficulty for listeners with hearing loss. We study the psychophysical limits of ability in these tasks, and we also study the neural coding and processing of these sounds using stimuli matched to those of our behavioral studies. Computational modeling helps bridge the gap between our behavioral and physiological studies. For example, using computational models derived from neural population recordings, we make predictions of behavioral abilities that can be directly compared to actual behavioral results. The cues and mechanisms used by our computational models can be manipulated to test different hypotheses for neural coding and processing.

By identifying the cues involved in the detection of signals in noise and fluctuations of signals, our goal is to direct novel strategies for signal processors to preserve, restore, or enhance these cues for listeners with hearing loss.