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

Traditionally, psychophysical data have been predicted either by constructing models of the peripheral auditory system or by applying signal detection theory (SDT). Frequently, the theoretical detection performance predicted by SDT is greater than that observed experimentally and a nonphysiologically based "internal noise" source is often added to the system to compensate for the discrepancy. A more appropriate explanation may be that traditional SDT approaches either incorporate little or no physiology or make simplifying assumptions regarding the density functions describing the physiological data. In the work presented here, an integrated approach, which combines SDT and a physiologically based model of the human auditory system, is proposed as an alternate method of quantifying detection performance. To validate this approach, the predicted detection performance for a simultaneous masking task is compared to predictions obtained from traditional methods and to experimental data. Additionally, the sensitivity of the integrated method is thoroughly investigated. The results suggest that by combining SDT with a physiologically based auditory model, thereby capitalizing on the strengths of each individual method, the previously observed discrepancies can be partially explained as the result of physical processes inherent in the auditory system rather than unspecified "internal noise" and more accurate predictions of psychophysical behavior can be obtained.

[1]  R. Patterson,et al.  Time-domain modeling of peripheral auditory processing: a modular architecture and a software platform. , 1995, The Journal of the Acoustical Society of America.

[2]  D. M. Green,et al.  Application of detection theory in psychophysics , 1970 .

[3]  L. A. Jeffress,et al.  Stimulus-oriented approach to detection re-examined. , 1967, Journal of the Acoustical Society of America.

[4]  Malvin C. Teich,et al.  Pulse‐number distribution for the neural spike train in the cat’s auditory nerve , 1985 .

[5]  W. C. Prothe,et al.  Detection of Signals in Noise: A Comparison between the Human Detector and an Electronic Detector , 1956 .

[6]  James P. Egan,et al.  Interval of Time Uncertainty in Auditory Detection , 1961 .

[7]  M B Sachs,et al.  Strategies for the representation of a tone in background noise in the temporal aspects of the discharge patterns of auditory-nerve fibers. , 1987, The Journal of the Acoustical Society of America.

[8]  Neal F. Viemeister,et al.  Intensity coding and the dynamic range problem , 1988, Hearing Research.

[9]  E D Young,et al.  Rate responses of auditory nerve fibers to tones in noise near masked threshold. , 1986, The Journal of the Acoustical Society of America.

[10]  Brian C. J. Moore,et al.  DETECTION OF DECREMENTS AND INCREMENTS IN SINUSOIDS AT HIGH OVERALL LEVELS , 1995 .

[11]  L. Braida,et al.  Towards a model for discrimination of broadband signals. , 1986, The Journal of the Acoustical Society of America.

[12]  L. Carney,et al.  A model for the responses of low-frequency auditory-nerve fibers in cat. , 1993, The Journal of the Acoustical Society of America.

[13]  Guido F. Smoorenburg,et al.  Combination Tones and Their Origin , 1972 .

[14]  R H Gilkey,et al.  Models of auditory masking: a molecular psychophysical approach. , 1986, The Journal of the Acoustical Society of America.

[15]  Charles S. Watson,et al.  Receiver‐Operating Characteristics Determined by a Mechanical Analog to the Rating Scale , 1964 .

[16]  Wilson P. Tanner,et al.  Theory of Signal Detectability as an Interpretive Tool for Psychophysical Data , 1960 .

[17]  Theodore G. Birdsall,et al.  Definitions of d′ and η as Psychophysical Measures , 1958 .

[18]  C. Mason,et al.  Roving-level tone-in-noise detection. , 1989, The Journal of the Acoustical Society of America.

[19]  L A Jeffress Mathematical and electrical models of auditory detection. , 1968, The Journal of the Acoustical Society of America.

[20]  P. Woodland,et al.  A computational model of the auditory periphery for speech and hearing research. II. Descending paths. , 1994, The Journal of the Acoustical Society of America.

[21]  L. A. Jeffress,et al.  Stimulus‐Oriented Approach to Detection , 1964 .

[22]  W A Yost,et al.  A time domain description for the pitch strength of iterated rippled noise. , 1996, The Journal of the Acoustical Society of America.

[23]  E. de Boer,et al.  On cochlear encoding: Potentialities and limitations of the reverse‐correlation technique , 1978 .

[24]  M. V. Mathews,et al.  Energy‐Detection Model for Monaural Auditory Detection , 1961 .

[25]  John A. Swets,et al.  On the Width of Critical Bands , 1962 .

[26]  J. L. Hall,et al.  Two-tone distortion products in a nonlinear model of the basilar membrane. , 1974, The Journal of the Acoustical Society of America.

[27]  T Dau,et al.  A quantitative model of the "effective" signal processing in the auditory system. I. Model structure. , 1996, The Journal of the Acoustical Society of America.

[28]  D G Pelli,et al.  Probe tone thresholds in the auditory nerve measured by two-interval forced-choice procedures. , 1987, The Journal of the Acoustical Society of America.

[29]  S M Forbes,et al.  Monaural detection with contralateral cue (MDCC). I. Better than energy detector performance by human observers. , 1969, The Journal of the Acoustical Society of America.