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Abstract
The aim of this study is to find and optimize a feature vector for an automatic recognition of the type of vehicles, extracted form an audio signal. First, the influence of weather-based conditions of road surface on spectral characteristic of the audio signal recorded from a passing vehicle in close proximity to the road is discussed. Next, parameterization of the recorded audio signal is performed. For that purpose, the MIRtoolbox, designed for music parameter extraction, is used to obtain a vector of parameters. Correlation analyses are performed to check whether extracted parameters enable to separate selected types of vehicle-associated noise, e.g.: car, truck and motorcycle. Behrens-Fisher statistics is used to find the most suitable parameters that may be contained in the optimized feature vector. The last step is to build a decision system that allows for the automatic classification of a vehicle type. The results of automatic classification of prepared vehiclenoise related samples are shown and discussed.
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
Journal of the Acoustical Society of America
no. 141,
pages 3883 - 3883,
ISSN: 0001-4966 - Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Marciniuk K., Kostek B., Czyżewski A.: Classifying type of vehicles on the basis of data extracted from audio signal characteristics// Journal of the Acoustical Society of America. -Vol. 141, nr. 5 (2017), s.3883-3883
- DOI:
- Digital Object Identifier (open in new tab) 10.1121/1.4988697
- Verified by:
- Gdańsk University of Technology
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