Title: Dimensionality reduction for acoustic vehicle classification with spectral embedding,
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.