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Tone-in-Noise Detection Using Envelope Cues: Comparison of Signal-Processing-Based and Physiological Models

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Abstract

Tone-in-noise detection tasks with reproducible noise maskers have been used to identify cues that listeners use to detect signals in noisy environments. Previous studies have shown that energy, envelope, and fine-structure cues are significantly correlated to listeners’ performance for detection of a 500-Hz tone in noise. In this study, envelope cues were examined for both diotic and dichotic tone-in-noise detection using both stimulus-based signal processing and physiological models. For stimulus-based envelope cues, a modified envelope slope model was used for the diotic condition and the binaural slope of the interaural envelope difference model for the dichotic condition. Stimulus-based models do not include key nonlinear transformations in the auditory periphery such as compression, rate and dynamic range adaptation, and rate saturation, all of which affect the encoding of the stimulus envelope. For physiological envelope cues, stimuli were passed through models for the auditory nerve (AN), cochlear nucleus, and inferior colliculus (IC). The AN and cochlear nucleus models included appropriate modulation gain, another transformation of the stimulus envelope that is not typically included in stimulus-based models. A model IC cell was simulated with a linear band-pass modulation filter. The average discharge rate and response fluctuations of the model IC cell were compared to human performance. Previous studies have predicted a significant amount of the variance across reproducible noise maskers in listeners’ detection using stimulus-based envelope cues. In this study, a physiological model that includes neural mechanisms that affect encoding of the stimulus envelope predicts a similar amount of the variance in listeners’ performance across noise maskers.

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ACKNOWLEDGMENTS

This work was supported NIH R01-DC010813.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Correspondence to Laurel H. Carney.

APPENDIX

APPENDIX

The sixth-order band-pass filter (H) was computed by cascading three second-order band-pass filters (H 1, H 2, and H 3). The formula for each second-order band-pass filter (H i ) is

$$ {H}_i(z)=\frac{1-{\alpha}_i}{2}\frac{1-{z}^{-2}}{1-{\beta}_i\left(1+{\alpha}_i\right){z}^{-1}+{\alpha}_i{z}^{-2}} $$
(A1)

where β is related to the center frequency, f i , of H i by

$$ {\beta}_i= \cos \left(2\pi\;{f}_i\right), $$
(A2)

and α is related to the 3-dB bandwidth, W i , by

$$ {\alpha}_i=\frac{1- \sin \left(2\pi\;{W}_i\right)}{ \cos \left(2\pi\;{W}_i\right)}. $$
(A3)

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Mao, J., Carney, L.H. Tone-in-Noise Detection Using Envelope Cues: Comparison of Signal-Processing-Based and Physiological Models. JARO 16, 121–133 (2015). https://doi.org/10.1007/s10162-014-0489-1

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  • DOI: https://doi.org/10.1007/s10162-014-0489-1

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