Vehicle Acoustic Classification in Netted Sensor Systems
Using Gaussian Mixture Models
December 2005
Burhan F. Necioglu, The MITRE Corporation
Carol T. Christou, The MITRE Corporation
E. Bryan George, The MITRE Corporation
Garry M. Jacyna, The MITRE Corporation
ABSTRACT
Acoustic vehicle classification is a difficult problem due to the non-stationary nature of the signals, and especially
the lack of strong harmonic structure for most civilian vehicles with highly muffled exhausts. Acoustic signatures
will also vary largely depending on speed, acceleration, gear position, and even the aspect angle of the sensor.
The problem becomes more complicated when the deployed acoustic sensors have less than ideal characteris-
tics, in terms of both the frequency response of the transducers, and hardware capabilities which determine the
resolution and dynamic range. In a hierarchical network topology, less capable Tier 1 sensors can be tasked
with reasonably sophisticated signal processing and classification algorithms, reducing energy-expensive communications with the upper layers. However, at Tier 2, more sophisticated classification algorithms exceeding the
Tier 1 sensor/processor capabilities can be deployed. The focus of this paper is the investigation of a Gaussian
mixture model (GMM) based classification approach for these upper nodes. The use of GMMs is motivated by
their ability to model arbitrary distributions, which is very relevant in the case of motor vehicles with varying
operation modes and engines. Tier 1 sensors acquire the acoustic signal and transmit computed feature vectors
up to Tier 2 processors for maximum-likelihood classification using GMMs. In a binary classification task of
light-vs-heavy vehicles, the GMM based approach achieves 7% equal error rate, providing an approximate error
reduction of 49% over Tier 1 only approaches.
Additional Search Keywords
Acoustics, feature extraction, Gaussian mixture models, distributed classification
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