Dimensionality reduction for acoustic vehicle classification with spectral embedding
Machine Learning
2018-02-20 v2 Machine Learning
Data Analysis, Statistics and Probability
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.
Cite
@article{arxiv.1705.09869,
title = {Dimensionality reduction for acoustic vehicle classification with spectral embedding},
author = {Justin Sunu and Allon G. Percus},
journal= {arXiv preprint arXiv:1705.09869},
year = {2018}
}
Comments
Proceedings of the 15th IEEE International Conference on Networking, Sensing and Control (2018)