Dimensionality reduction for acoustic vehicle classification with spectral embedding
This addresses traffic analysis and surveillance needs, but it is incremental as it adapts existing methods to a specific domain.
The authors tackled vehicle classification using roadside audio sensors by applying spectral embedding to reduce dimensionality, achieving accurate identification with K-nearest neighbors.
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.