We combine neurophysiological, behavioral, and computational modeling techniques towards our goal of understanding neural mechanisms underlying the perception of complex sounds. Our work now includes studies of both listeners with normal hearing ability and those with sensorineural hearing loss. 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 three specific problems: detection of acoustic signals in background noise, detection of fluctuations in the amplitude of sounds, and neural coding of vowels. 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, fluctuations of signals, and coding of vowels, our goal is to direct novel strategies for signal processors to preserve, restore, or enhance these cues for listeners with hearing loss.
Selected Relevant Recent Publications:
Davidson, S.A., Gilkey, R.H., Colburn, H.S. and Carney, L.H. (2006), Binaural detection with narrowband and wideband reproducible noise maskers: III. Models for monaural and diotic detection, J. Acoust. Soc. Am. 119:2258-2275
Anzalone, M.C., Calandruccio, L., Doherty, K.A., Carney, L.H., (2006), Determination of the Potential Benefit of Time-Frequency Gain Manipulation, Ear & Hearing. 27: 480-492.
Nelson, P.C., and Carney, L. H. (2006) Cues for masked amplitude-modulation detection, J. Acoust. Soc. Am. 120, 978-990.
Tan and Carney (2006), Predictions of Formant-Frequency Discrimination in Noise Based on Model Auditory-Nerve Responses, J. Acoust. Soc. Am. 120:1435-1445.
Gai, Y. and Carney, L.H. (2006) Temporal measures and neural strategies for detection of tones in noise based on responses in anteroventral cochlear nucleus, J. Neurophysiol., 96:2451-2464.
Nelson, P.C. and Carney, L. H. (2007) Rate and timing cues for neural detection and discrimination of amplitude-modulated tones in the awake rabbit inferior colliculus. J. Neurophysiol. 97:522-539.
Nelson, P.N. Ewert, S.D., Carney, L.H., and Dau, T. (2007). Comparison of intensity discrimination, increment detection, and comodulation masking release in the audio- and envelope-frequency domains, J. Acoust. Soc. Am. 121:2168-2181.
Deshmukh, O., Espy-Wilson, C., and Carney, L.H. (2007) Speech enhancement using the modified phase-opponency model, J. Acoust. Soc. Am. 121: 3886-3898.
Calandruccio, L., Doherty, K.A., Carney, L.H., Kikkeri, H.N. (2007), Perception of Temporally Processed Speech by Listeners with Hearing Impairment, Ear & Hearing. 28: 512-523
Gai, Y., L.H. Carney, K.S. Abrams, F. Idrobo, J. M. Harrison, R. H. Gilkey (2007) Detection of Tones in Reproducible Noise Maskers by Rabbits and Comparison to Detection by Humans, JARO, 8: 1525-3961.
Gai, Y., L.H. Carney (2008) Influence of Inhibitory Inputs on Rate and Timing of Responses in the Anteroventral Cochlear Nucleus, J. Neurophysiol., 99:1077-1095.
Gai, Y., L.H. Carney (2008) Statistical Analyses of Temporal Information in Auditory Brainstem Responses to Tones in Noise: Correlation Index and Spike-distance Metric, JARO, 9:373-387.