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Auditory models and nonlinear filterbanks in underwater auralization

Auditory models and nonlinear filterbanks in underwater auralization
Auditory models and nonlinear filterbanks in underwater auralization
Mammals like dolphins and humans have the ability to distinguish between different objects by listening to their scattered signals. We explore this phenomenon by evaluating the "Auditory Image Model" (AIM), a biologically inspired model of the human auditory system from the outer ear up to central processing. AIM aims to simulate the spectral analysis of the basilar membrane, the neural encoding and the temporal integration performed by the auditory system. Simulated scattered signals from the same object objects of different sizes and different objects of same size subjected to an incident pulse were analysed numerically with AIM, simulating the response of the human auditory system. The resulting neuronal activity patterns were analysed in two independent dimensions using the filter centre frequency and the relative timing intervals of the responses. The results are interpreted globally as reflecting the size and the shape-slash properties of the object respectively. We show for a series of calibration stimuli that the model can distinguish between same objects of different size and different objects of the same size. We also discuss how a nonlinear model of the basilar membrane, performing a Gammachirp-slash Mellin transformation, may act as a possible feature extraction tool for classification methods ©2008 Acoustical Society of America

3344-3344
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c
Fox, Paul D.
462039ef-2dd7-4dd0-8f4d-f853ffdf990a
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
O'Meara, Niamh
2e65eef4-0a13-49a6-b808-bc3d944f1b9d
Bleeck, Stefan
c888ccba-e64c-47bf-b8fa-a687e87ec16c
Fox, Paul D.
462039ef-2dd7-4dd0-8f4d-f853ffdf990a
White, Paul R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
O'Meara, Niamh
2e65eef4-0a13-49a6-b808-bc3d944f1b9d

Bleeck, Stefan, Fox, Paul D., White, Paul R. and O'Meara, Niamh (2008) Auditory models and nonlinear filterbanks in underwater auralization. p. 3344 . (doi:10.1121/1.2933893).

Record type: Conference or Workshop Item (Other)

Abstract

Mammals like dolphins and humans have the ability to distinguish between different objects by listening to their scattered signals. We explore this phenomenon by evaluating the "Auditory Image Model" (AIM), a biologically inspired model of the human auditory system from the outer ear up to central processing. AIM aims to simulate the spectral analysis of the basilar membrane, the neural encoding and the temporal integration performed by the auditory system. Simulated scattered signals from the same object objects of different sizes and different objects of same size subjected to an incident pulse were analysed numerically with AIM, simulating the response of the human auditory system. The resulting neuronal activity patterns were analysed in two independent dimensions using the filter centre frequency and the relative timing intervals of the responses. The results are interpreted globally as reflecting the size and the shape-slash properties of the object respectively. We show for a series of calibration stimuli that the model can distinguish between same objects of different size and different objects of the same size. We also discuss how a nonlinear model of the basilar membrane, performing a Gammachirp-slash Mellin transformation, may act as a possible feature extraction tool for classification methods ©2008 Acoustical Society of America

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More information

Published date: May 2008
Organisations: Human Sciences Group

Identifiers

Local EPrints ID: 57857
URI: http://eprints.soton.ac.uk/id/eprint/57857
PURE UUID: ce8ca683-078d-47b2-85a5-33b92e2f5a5e
ORCID for Stefan Bleeck: ORCID iD orcid.org/0000-0003-4378-3394
ORCID for Paul R. White: ORCID iD orcid.org/0000-0002-4787-8713

Catalogue record

Date deposited: 20 Aug 2008
Last modified: 11 Jul 2024 01:44

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Contributors

Author: Stefan Bleeck ORCID iD
Author: Paul D. Fox
Author: Paul R. White ORCID iD
Author: Niamh O'Meara

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