Challenges of Vehicle Classification Using Acoustics
Automated acoustic classification of vehicles is a challenging problem with many variables. Vehicles produce complex sounds from many sources, sound signatures vary between similar vehicles, and background noise has a large impact on audio data. Past research studied different vehicle classification...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130658 |
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| author | Raiden Evans Stephanie Mullins George Noel |
| author_facet | Raiden Evans Stephanie Mullins George Noel |
| author_sort | Raiden Evans |
| collection | DOAJ |
| description | Automated acoustic classification of vehicles is a challenging problem with many variables. Vehicles produce complex sounds from many sources, sound signatures vary between similar vehicles, and background noise has a large impact on audio data. Past research studied different vehicle classification techniques but often relied on datasets with little variation in vehicle model, environmental conditions, or microphones. High-accuracy results on these datasets suggest issues of overfitting. This paper highlights the challenges of creating robust datasets for difficult acoustic classification tasks. Challenges include collecting on multiple instances of a target class, recording with varied environmental noise, and collecting across multiple types of microphones. The work presented also evaluates classification performance on combinations of acoustic feature sets and common machine learning algorithms. Classification F-score performance drops 38.67% when the test set has background noise that differs from the classifier’s training set. Performance also significantly drops when the classifier is tested on a vehicle of a model year not included in the training set. Lastly, the vehicle classifier heavily overfits on the signatures of the microphones it is trained on. |
| format | Article |
| id | doaj-art-71edf75fafbd4cedb5b9cca17bf111ee |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-71edf75fafbd4cedb5b9cca17bf111ee2025-08-20T03:05:26ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622022-05-013510.32473/flairs.v35i.13065866857Challenges of Vehicle Classification Using AcousticsRaiden Evans0Stephanie Mullins1George Noel2Infoscitex CorporationInfoscitex CorporationInfoscitex CorporationAutomated acoustic classification of vehicles is a challenging problem with many variables. Vehicles produce complex sounds from many sources, sound signatures vary between similar vehicles, and background noise has a large impact on audio data. Past research studied different vehicle classification techniques but often relied on datasets with little variation in vehicle model, environmental conditions, or microphones. High-accuracy results on these datasets suggest issues of overfitting. This paper highlights the challenges of creating robust datasets for difficult acoustic classification tasks. Challenges include collecting on multiple instances of a target class, recording with varied environmental noise, and collecting across multiple types of microphones. The work presented also evaluates classification performance on combinations of acoustic feature sets and common machine learning algorithms. Classification F-score performance drops 38.67% when the test set has background noise that differs from the classifier’s training set. Performance also significantly drops when the classifier is tested on a vehicle of a model year not included in the training set. Lastly, the vehicle classifier heavily overfits on the signatures of the microphones it is trained on.https://journals.flvc.org/FLAIRS/article/view/130658vehicle acousticsmachine learningartificial intelligenceoverfittingvehicle classificationacoustic classificationcyber physical sensingcyber physical fused sensingcpscpfs |
| spellingShingle | Raiden Evans Stephanie Mullins George Noel Challenges of Vehicle Classification Using Acoustics Proceedings of the International Florida Artificial Intelligence Research Society Conference vehicle acoustics machine learning artificial intelligence overfitting vehicle classification acoustic classification cyber physical sensing cyber physical fused sensing cps cpfs |
| title | Challenges of Vehicle Classification Using Acoustics |
| title_full | Challenges of Vehicle Classification Using Acoustics |
| title_fullStr | Challenges of Vehicle Classification Using Acoustics |
| title_full_unstemmed | Challenges of Vehicle Classification Using Acoustics |
| title_short | Challenges of Vehicle Classification Using Acoustics |
| title_sort | challenges of vehicle classification using acoustics |
| topic | vehicle acoustics machine learning artificial intelligence overfitting vehicle classification acoustic classification cyber physical sensing cyber physical fused sensing cps cpfs |
| url | https://journals.flvc.org/FLAIRS/article/view/130658 |
| work_keys_str_mv | AT raidenevans challengesofvehicleclassificationusingacoustics AT stephaniemullins challengesofvehicleclassificationusingacoustics AT georgenoel challengesofvehicleclassificationusingacoustics |