Models that contain the Implementer : Tan, Qing

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    Models   Description
1.  Auditory nerve response model (Tan, Carney 2003)
A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing.
2.  Encoding and discrimination of vowel-like sounds (Tan and Carney 2005)
"The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as a function of the center frequency of the complex cannot be explained by changes in the level of the stimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943–1955 (1995)]. Rather, a complex pattern of sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when the center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope, greatest sensitivity occurs when the center frequency is aligned with a harmonic. In this study, the thresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to these trends in psychophysical thresholds. Single-fiber and population model responses were evaluated in terms of both average discharge rate and the combination of rate and timing information. ..."
3.  Predicting formant-frequency discrimination in noise (Tan and Carney 2006)
"To better understand how the auditory system extracts speech signals in the presence of noise, discrimination thresholds for the second formant frequency were predicted with simulations of auditory-nerve responses. These predictions employed either average-rate information or combined rate and timing information, and either populations of model fibers tuned across a wide range of frequencies or a subset of fibers tuned to a restricted frequency range. In general, combined temporal and rate information for a small population of model fibers tuned near the formant frequency was most successful in replicating the trends reported in behavioral data for formant-frequency discrimination. ..."

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