Dimensionality reduction for acoustic vehicle classification with spectral embedding

Autor:	Sunu, Justin, Percus, Allon G.
Rok vydání:	2017
Předmět:	Statistics - Machine Learning Computer Science - Learning Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	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. Comment: Proceedings of the 15th IEEE International Conference on Networking, Sensing and Control (2018)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1705.09869 Zobrazit plný text záznamu View this record from Arxiv