The sound (engine, noise, etc.) of a working vehicle provides an important clue, e.g., for surveillance mission robots, to recognize the vehicle type. In this paper, we introduce the "eigenfaces method", originally used in human face recognition, to model the sound frequency distribution features. We show that it can be a simple and reliable acoustic identification method if the training samples can be properly chosen and classified. We treat the frequency spectra of about 200 ms of sound (a "frame") as a vector in a high-dimensional frequency feature space. In this space, we study the vector distribution for each kind of vehicle sound produced under similar working conditions. A collection of typical sound samples is used as the training data set. The mean frequency vector of the training set is first calculated, and subtracted from each vector in the set. To capture the frequency vectors' variation within the training set, we then calculate the eigenvectors of the covariance matrix of the zero-mean-adjusted sample data set. These eigenvectors represent the principal components of the vector distribution: for each such eigenvector, its corresponding eigenvalue indicates its importance in capturing the variation distribution, with the largest eigenvalues accounting for the most variance within this data set. Thus for each set of training data, its mean vector and its moat important eigenvectors together characterize its sound signature. When a new frame (not in the training set) is tested, its spectrum vector is compared against the mean vector; the difference vector is then projected into the principal component directions, and the residual is found. The coefficients of the unknown vector, in the training set eigenvector basis subspace, identify the unknown vehicle noise in terms of the classes represented in the training set. The magnitude of the residual vector measures the extent to which the unknown vehicle sound cannot be well characterized by the vehicle sounds included in the training set.
['Huadong Wu', 'Mel Siegel', 'Pradeep K. Khosla']
Vehicle sound signature recognition by frequency vector principal component analysis