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Titolo:
Using computational auditory models to predict simultaneous masking data: Model comparison
Autore:
Huettel, LG; Collins, LM;
Indirizzi:
Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA Duke Univ Durham NC USA 27708 ept Elect & Comp Engn, Durham, NC 27708 USA
Titolo Testata:
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
fascicolo: 12, volume: 46, anno: 1999,
pagine: 1432 - 1440
SICI:
0018-9294(199912)46:12<1432:UCAMTP>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
QUANTITATIVE MODEL; NERVE FIBERS; SYSTEM; DISCRIMINATION; RESPONSES; NOISE;
Keywords:
computational auditory model; signal detection theory; simultaneous masking;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
26
Recensione:
Indirizzi per estratti:
Indirizzo: Collins, LM Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA Duke Univ Durham NC USA 27708 Comp Engn, Durham, NC 27708 USA
Citazione:
L.G. Huettel e L.M. Collins, "Using computational auditory models to predict simultaneous masking data: Model comparison", IEEE BIOMED, 46(12), 1999, pp. 1432-1440

Abstract

In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology, One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates [15], [1], To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks [20]. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue tb exceed experimentally-measured performance [9], [21]. In this paper, we compare predictions of detection performance across several computational auditory models. We also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.

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Documento generato il 06/07/20 alle ore 04:56:20