Using computational auditory models to predict simultaneous masking data: model comparison.
Using computational auditory models to predict simultaneous masking data: model comparison.
复制标题
使用计算听觉模型来预测同时掩蔽数据：模型比较。
DOI：
10.1109/10.804571
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Collins,LM
中科院分区：
文献类型：
--
作者：
Huettel,LG;Collins,LM
In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks. Previous efforts, including the authors' own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue to exceed experimentally-measured performance. Here, the authors compare predictions of detection performance across several computational auditory models. They also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.
