Analysis of the performance of a model-based optimal auditory signal processor

Gresham, Lisa C.; Collins, Leslie M.

doi:10.1121/1.422773

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…It produces post stimulus time histograms that agree well with published data recorded from single auditory fibers [19]. In our previous work [13,14], we applied signal detection theory to the output of the "neural firing" stage of AIM, which produces an average firing rate. In so doing, we developed the probability density function (pdf) for the neural firing rates under the "signal" and "no signal" hypotheses.…”

Section: Theoretical Predictions Of Experimental Performancesupporting

confidence: 67%

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Enhanced auditory processing for land mine detection using EMI sensors

et al. 1999

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Although the ability of EMI sensors to detect landmines has improved significantly, false alarm rate reduction remains a challenging problem. Improvements have been achieved through development of optimal algorithms that exploit models of the underlying physics along with knowledge of clutter statistics. Moreover, experienced operators can often discriminate mines from clutter with the aid of an audio transducer, the method most commonly used to alert the sensor operator that a target is present. Assuming the basic information needed for discriminating landmines from clutter is largely available from existing sensors, the goal of this work is to optimize the presentation of information to the operator. Traditionally, an energy calculation is provided to the sensor operator via a signal whose loudness or frequency is proportional to the energy of the received signal. Our preliminary work has shown that when the statistic used to make a decision is not simply the signal energy the performance of mine detection systems can be improved dramatically. This fmding suggests that the operator could make better use of a signal that is a function of this more accurate test statistic, and that there may be information in the unprocessed sensor signal that the operator could use to effect discrimination. In this paper, we investigate and quantify, through listening experiments, the perceptual dimensions that most effectively convey the information in a sensor response to a listener. Results indicate that by supplying the sensor response more appropriately to the listener, discrimination, as opposed to simple detection, can be achieved.

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Section: Theoretical Predictions Of Experimental Performancesupporting

confidence: 67%

“…Recent work has demonstrated that using signal detection theory to analyze the signals produced by these models yields more accurate predictions of detection performance [13,14]. However, in these studies, discrepancies still remained between theoretical predictions and experimental data.…”

Section: Theoretical Predictions Of Experimental Performancementioning

confidence: 99%

Enhanced auditory processing for land mine detection using EMI sensors

et al. 1999

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“…This article focuses on extension of the SDT approach to allow the use of computational models to predict psychophysical performance limits for auditory discrimination based on information encoded in the stochastic AN discharge patterns. Several studies have used SDT to relate computational auditory models to psychophysical performance (Dau, Püschel, & Kohlrausch, 1996aDau, Kollmeier, & Kohlrausch, 1997a, 1997bGresham & Collins, 1998;Huettel & Collins, 1999). Dau et al (1996aDau et al ( , 1996bDau et al ( , 1997aDau et al ( , 1997b developed computational models of effective auditory processing with the goal of matching predicted and human performance (i.e., in terms of both absolute values and trends for various stimulus parameters).…”

Section: Combining Computational Models With Signal Detection The-mentioning

confidence: 99%

“…Their auditory models are physiologically motivated but are not intended to describe the processing at speci c locations in the auditory pathway, and therefore are not compared directly to physiological responses. Gresham and Collins (1998) and Huettel and Collins (1999) used SDT to evaluate information loss at different stages of several more physiologically based computational auditory models. Psychophysical performance was limited in their analysis only by the random variability associated with the noise stimulus (i.e., their analysis did not include any form of internal physiological noise).…”

Section: Combining Computational Models With Signal Detection The-mentioning

confidence: 99%

Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

2001

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A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. The use of computational models allows predictions to be made over a wider parameter range and with more complete descriptions of AN responses than in analytical models. Performance based on AN discharge times (all-information) is compared to performance based only on discharge counts (rate-place). After the method is verified over the range of parameters for which previous analytical models are applicable, the parameter space is then extended. For example, a computational model of AN activity that extends to high frequencies is used to explore the common belief that rate-place information is responsible for frequency encoding at high frequencies due to the rolloff in AN phase locking above 2 kHz. This rolloff is thought to eliminate temporal information at high frequencies. Contrary to this belief, results of this analysis show that rate-place predictions for frequency discrimination are inconsistent with human performance in the dependence on frequency for high frequencies and that there is significant temporal information in the AN up to at least 10 kHz. In fact, the all-information predictions match the functional dependence of human performance on frequency, although optimal performance is much better than human performance. The use of computational AN models in this study provides new constraints on hypotheses of neural encoding of frequency in the auditory system; however, the method is limited to simple tasks with deterministic stimuli. A companion article in this issue ("Evaluating Auditory Performance Limits: II") describes an extension of this approach to more complex tasks that include random variation of one parameter, for example, random-level variation, which is often used in psychophysics to test neural encoding hypotheses.

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“…Computational neural models can describe more accurate physiological responses to a much wider range of stimuli than analytical models. Previous methods that have combined SDT and computational auditory models to predict psychophysical performance have either not included physiological (internal) noise (e.g., Gresham & Collins, 1998;Huettel & Collins, 1999) or have used arbitrary internal noise that was not directly related to physiological variability (e.g., Dau, Püschel, & Kohlrausch, 1996a, 1996bDau, Kollmeier, & Kohlrausch, 1997a, 1997b. Our companion article in this issue ("Evaluating Auditory Performance Limits: II") describes a general method that extends previous studies that have quanti ed the effects of physiological noise on psychophysical performance using analytical auditory nerve (AN) models (e.g., Siebert, 1968Siebert, , 1970Colburn, 1969Colburn, , 1973 to incorporate the use of computational models; however, the SDT analysis in the companion article was limited to deterministic discrimination experiments.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Auditory Performance Limits: II. One-Parameter Discrimination with Random-Level Variation

2001

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Previous studies have combined analytical models of stochastic neural responses with signal detection theory (SDT) to predict psychophysical performance limits; however, these studies have typically been limited to simple models and simple psychophysical tasks. A companion article in this issue ("Evaluating Auditory Performance Limits: I") describes an extension of the SDT approach to allow the use of computational models that provide more accurate descriptions of neural responses. This article describes an extension to more complex psychophysical tasks. A general method is presented for evaluating psychophysical performance limits for discrimination tasks in which one stimulus parameter is randomly varied. Psychophysical experiments often randomly vary a single parameter in order to restrict the cues that are available to the subject. The method is demonstrated for the auditory task of random-level frequency discrimination using a computational auditory nerve (AN) model. Performance limits based on AN discharge times (all-information) are compared to performance limits based only on discharge counts (rate place). Both decision models are successful in predicting that random-level variation has no effect on performance in quiet, which is the typical result in psychophysical tasks with random-level variation. The distribution of information across the AN population provides insight into how different types of AN information can be used to avoid the influence of random-level variation. The rate-place model relies on comparisons between fibers above and below the tone frequency (i.e., the population response), while the all-information model does not require such across-fiber comparisons. Frequency discrimination with random-level variation in the presence of high-frequency noise is also simulated. No effect is predicted for all-information, consistent with the small effect in human performance; however, a large effect is predicted for rate-place in noise with random-level variation.

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Analysis of the performance of a model-based optimal auditory signal processor

Cited by 12 publications

References 31 publications

Enhanced auditory processing for land mine detection using EMI sensors

Enhanced auditory processing for land mine detection using EMI sensors

Evaluating Auditory Performance Limits: I. One-Parameter Discrimination Using a Computational Model for the Auditory Nerve

Evaluating Auditory Performance Limits: II. One-Parameter Discrimination with Random-Level Variation

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