基本信息
浏览量:12
职业迁徙
个人简介
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.
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.
研究兴趣
论文共 200 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
NONLINEARITY AND HEARING: ADVANCES IN THEORY AND EXPERIMENT: Proceedings of the 14th International Mechanics of Hearing Workshop AIP Conference Proceedings (2024)
HEARING RESEARCH (2024): 108966-108966
biorxiv(2024)
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICAno. 3 (2023): A334-A334
bioRxiv (Cold Spring Harbor Laboratory) (2023): 108767-108767
Daniel R. Guest,Laurel H. Carney
biorxiv(2023)
Journal of the Acoustical Society of Americano. 3_supplement (2023): A115-A115
引用0浏览0引用
0
0
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICAno. 4 (2023): 1994-2005
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn