Extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification

In this study we address automatic vehicle and engine identification based on audio information. This information be based on many factors, such as vehicle type, tires, speed, wear and tear of vehicles, as well as type of road. We have decided a feature set for discriminating pairs of classes. Featu...

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Published inRecent Trends in Image Processing and Pattern Recognition Vol. 1576; pp. 338 - 345
Main Authors Sinha, Amandeep, Kumar, S. Hemanth, Prabhakar, Gudmalwar Ashishkumar, Rao, Ch V. Rama
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2022
Springer International Publishing
SeriesCommunications in Computer and Information Science
Subjects
CNN
Pattern recognition
Temporal features
Vehicle sound
Online AccessGet full text
ISBN3031070046
9783031070044
ISSN1865-0929
1865-0937
DOI10.1007/978-3-031-07005-1_29

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Abstract In this study we address automatic vehicle and engine identification based on audio information. This information be based on many factors, such as vehicle type, tires, speed, wear and tear of vehicles, as well as type of road. We have decided a feature set for discriminating pairs of classes. Feature set include Fibonacci feature space, entropy, skewness and kurtosis. The audio information collected are real time on-road recordings. There are four classes of vehicle sounds. The paper also shows problems related to vehicles classification. Classification on audio-based engine and vehicle type identification are proposed and conclusions are shown.
AbstractList In this study we address automatic vehicle and engine identification based on audio information. This information be based on many factors, such as vehicle type, tires, speed, wear and tear of vehicles, as well as type of road. We have decided a feature set for discriminating pairs of classes. Feature set include Fibonacci feature space, entropy, skewness and kurtosis. The audio information collected are real time on-road recordings. There are four classes of vehicle sounds. The paper also shows problems related to vehicles classification. Classification on audio-based engine and vehicle type identification are proposed and conclusions are shown.
Author Kumar, S. Hemanth
Prabhakar, Gudmalwar Ashishkumar
Sinha, Amandeep
Rao, Ch V. Rama
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Snippet In this study we address automatic vehicle and engine identification based on audio information. This information be based on many factors, such as vehicle...
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StartPage 338
SubjectTerms CNN
Pattern recognition
Temporal features
Vehicle sound
Title Extraction of Temporal Features on Fibonacci Space for Audio Based Vehicle Classification
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