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Improving acoustic vehicle classification by information fusion

Improving acoustic vehicle classification by information fusion
Improving acoustic vehicle classification by information fusion
We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approach
Pattern classification, Bayesian decisionfusion, Information fusion, Vehicle Recognition
29-43
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Damarla, Thyagaraju
11ab9e60-9c7c-4d73-bbf7-8cb2ac85e311
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Damarla, Thyagaraju
11ab9e60-9c7c-4d73-bbf7-8cb2ac85e311

Guo, Baofeng, Nixon, Mark and Damarla, Thyagaraju (2011) Improving acoustic vehicle classification by information fusion. Pattern Analysis and Applications, 15 (1), 29-43. (doi:10.1007/s10044-011-0202-5).

Record type: Article

Abstract

We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approach

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Published date: March 2011
Keywords: Pattern classification, Bayesian decisionfusion, Information fusion, Vehicle Recognition
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 272713
URI: http://eprints.soton.ac.uk/id/eprint/272713
PURE UUID: a697cb10-c338-4d1a-af57-78768e99929a
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 24 Aug 2011 16:09
Last modified: 17 Dec 2019 02:04

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