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

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 finestructure 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. Stimulusbased 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 stimulusbased 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.

Midbrain Synchrony to Envelope Structure Supports Behavioral Sensitivity to Single-Formant Vowel-Like Sounds in Noise

“…The contribution of band-enhanced modulation tuning in the IC to average-rate coding of vowel-like sounds was further supported by the modeling results. Phenomenological models of the mammalian auditory nerve and IC (Zilany et al 2014;Mao and Carney 2015) were able to reproduce the averagerate representation of formant frequency observed in our IC recordings, without any adjustment of model parameters, from a primarily synchrony-based representation in the peripheral input stage. Bandenhanced modulation tuning in this implementation of the IC model arises from simple band-pass filtering of the time-varying instantaneous discharge rate of a model auditory nerve fiber, but equivalently, could arise through putative fast excitation coupled with stronger, delayed inhibition from lemniscal input fibers with similar BFs (Nelson and Carney 2004;Nelson and Carney 2007;Mao and Carney 2015).…”

“…Phenomenological models of the mammalian auditory nerve and IC (Zilany et al 2014;Mao and Carney 2015) were able to reproduce the averagerate representation of formant frequency observed in our IC recordings, without any adjustment of model parameters, from a primarily synchrony-based representation in the peripheral input stage. Bandenhanced modulation tuning in this implementation of the IC model arises from simple band-pass filtering of the time-varying instantaneous discharge rate of a model auditory nerve fiber, but equivalently, could arise through putative fast excitation coupled with stronger, delayed inhibition from lemniscal input fibers with similar BFs (Nelson and Carney 2004;Nelson and Carney 2007;Mao and Carney 2015). This basic model structure appears physiologically plausible, considering that it reproduces commonly observed changes in AM response properties observed with pharmacological blockage of inhibitory inputs in the IC, including elevation of discharge rate across modulation frequencies with minimal change in BMF (Burger and Pollak 1998;Caspary et al 2002;Zhang and Kelly 2003).…”

“…Furthermore, the model incorporates power law dynamics and long-term adaptation at the hair cell synapse that allow accurate prediction of responses to envelope periodicity and forward masking. The IC model (Mao and Carney 2015) generates band-enhanced average rate responses to AM stimuli through band-pass filtering of the time varying output of a single model auditory nerve fiber. Single-formant responses were predicted for the same band-limited harmonic tone complexes used in the behavioral experiments and neurophysiological recordings.…”

“…Phenomenological models of the auditory nerve (Zilany et al 2014) and band-enhanced modulation tuning in the IC (Mao and Carney 2015) were used to gain insight into the contribution of rate-based modulation tuning to neural coding of formant frequency. Model auditory nerve fibers with characteristic frequencies near 2 kHz showed subtle fluctuations in average discharge rate with increasing formant frequency in quiet (Fig.…”

Temporal weighting of binaural information at low frequencies: Discrimination of dynamic interaural time and level differences

“…A number of potential mechanisms might contribute, including destructive interference on the basilar membrane (Tollin, 1998) or adaptation in the neuronal inputs to binaural comparison (as suggested by Hafter, 1997;Dietz et al, 2014). More central mechanisms could also potentially contribute, such as interactions between envelope sensitivity and binaural tuning among neurons of the superior olivary complex (Remme et al, 2014) and inferior colliculus (Mao and Carney, 2015).…”