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Carnegie Mellon Robotics Institute

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Abstract

The sound of a working vehicle provides an important clue to 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 categorized. We treat the frequency spectrum in a 200 ms time interval (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 vector and the most important principal component eigenvectors of the covariance matrix of the zero-mean-adjusted samples together characterize its sound signature. When a new zero-mean-adjusted sample is projected into the principal component eigenvector directions, a small residual vector indicates that the unknown vehicle sound can be well characterized in terms of the training data set.

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Intelligent Sensor, Measurement, and Control Lab

Associated Project(s): Vehicle Sound Pattern Recognition

Text Reference

Huadong Wu, Mel Siegel, and Pradeep Khosla, "Vehicle sound signature recognition by frequency vector principal component analysis," IEEE Transactions on Instrumentation and Measurement, Vol. 48, No. 5, October, 1999, pp. 1005 - 1009.

BibTeX Reference

@article{Wu_1999_3563,
   author = "Huadong Wu and Mel Siegel and Pradeep Khosla",
   title = "Vehicle sound signature recognition by frequency vector principal component analysis",
   journal = "IEEE Transactions on Instrumentation and Measurement",
   pages = "1005 - 1009",
   month = "October",
   year = "1999",
   volume = "48",
   number = "5",
}

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