  Vehicle recognition and classification have broad applications, ranging from
traffic flow management to military target identification. We demonstrate an
unsupervised method for automated identification of moving vehicles from
roadside audio sensors. Using a short-time Fourier transform to decompose audio
signals, we treat the frequency signature in each time window as an individual
data point. We then use a spectral embedding for dimensionality reduction.
Based on the leading eigenvectors, we relate the performance of an incremental
reseeding algorithm to that of spectral clustering. We find that incremental
reseeding accurately identifies individual vehicles using their acoustic
signatures.
