IDENTIFICATION OF ACOUSTIC SIGNATURES FOR VEHICLES VIA REDUCTION OF DIMENSIONALITY.

In this paper we propose a robust algorithm that solves two related problems: (1) Classification of acoustic signals emitted by different moving vehicles. The recorded signals have to be identified to which pre-existing group they belong to independently of the recording surrounding conditions. (2) Detection of the presence of a vehicle in a certain class via analysis of its acoustic signature against the existing database of recorded and processed acoustic signals. To achieve this detection with minimal false alarms we construct the acoustic signature of a certain vehicle using the distribution of the energies among blocks which consist of coefficients of multiscale local cosine transform (LCT) applied in the frequency domain of the acoustic signal. The proposed algorithm is robust even under severe noise and diverse rough surrounding conditions. This is a generic technology, which has many algorithmic variations, can be used to solve wide range of classification and detection problems which are based on a unique derivation of signatures.