Classification of ground vehicles using acoustic signal processing and neural network classifier

Abstract

Almost all moving vehicles generate some kind of noise that can be due to the vibrations of engine, rotational parts, bumping and friction of the vehicle tires with the ground, wind effects, gears, fans etc. this sound provides an important clue or characteristic pattern to recognize the vehicle type. Similar vehicles working in comparable conditions would have a similar acoustic signature that could be used for recognition. Characteristic patterns may be extracted either in time domain or frequency domain or a combination of these two i.e. time frequency domain. Classification of ground vehicles based on acoustic signals can be employed effectively in battlefield surveillance, traffic control, military and many other applications. In this paper we present efficient and less complex method for feature extraction in time domain with the help of Fourier transform. The recorded signals and their feature vectors have to be stored and assigned to pre-existing categories or classes i.e. these feature vectors will give us our database in matrix form, which is used for vehicle classification in neural network classifier.