UR_EAR_2020b: New Release of UR_EAR Matlab tool for visualizing model Auditory-nerve and Midbrain responses. This new version includes more stimulus options, new features, and both the Zilany et al. 2014 and Bruce et al 2018 AN models.
We study hearing! We’re interested in the incredible ability of the healthy auditory system to detect and understand sounds, even in noisy backgrounds. We’re also interested in understanding how the auditory system encodes speech sounds. The holy grail of hearing science is to help listeners with hearing loss, and the biggest challenge for these listeners is understanding speech in noise.
We use many experimental and computational techniques in an effort to better understand hearing and hearing loss. We combine neurophysiological, behavioral, and computational modeling techniques towards our goal of understanding the neural mechanisms supporting perception of complex sounds. Computational modeling bridges the gap between our behavioral and physiological studies. For example, using computational models based on neural recordings, we make predictions of behavioral abilities. These predictions can be directly compared to actual behavioral results. The cues and mechanisms used by our computational models can be varied to test different hypotheses for neural coding and processing.
We are also interested in applying our results to the design of novel signal-processing strategies to enhance speech, especially for listeners with hearing loss. By identifying the acoustic cues involved in the detection of signals in noise and in coding speech sounds, we can devise strategies to preserve, restore, or enhance these cues for listeners with hearing loss.