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• W2963670053 abstract "We propose a method for recognizing moving vehicles, using data from roadside audio sensors. This problem has applications ranging widely, from traffic analysis to surveillance. We extract a frequency signature from the audio signal using a short-time Fourier transform, and treat each time window as an individual data point to be classified. By applying a spectral embedding, we decrease the dimensionality of the data sufficiently for K-nearest neighbors to provide accurate vehicle identification." @default.
• W2963670053 created "2019-07-30" @default.
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• W2963670053 date "2018-03-01" @default.
• W2963670053 modified "2023-09-24" @default.
• W2963670053 title "Dimensionality reduction for acoustic vehicle classification with spectral embedding" @default.
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• W2963670053 doi "https://doi.org/10.1109/icnsc.2018.8361290" @default.
• W2963670053 hasPublicationYear "2018" @default.
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