Spectral features for the classification of civilian vehicles using acoustic sensors

Identification of civilian vehicles is in increasing demand in military and homeland security applications such as surveillance and traffic monitoring. This paper presents a new algorithm to estimate the fundamental frequency of a car engine sound accurately. It also presents examples of the use of this frequency to identify and classify different civilian vehicles. Acoustic features, such as the tristimulus response introduced in music theory, are proposed as tools for the characterization of the timbre of the car engine sound. In addition we introduce a new generalized M-stimulus as a tool to distinguish special features in some car engines.

[1]  Kie B. Eom,et al.  Analysis of Acoustic Signatures from Moving Vehicles Using Time-Varying Autoregressive Models , 1999, Multidimens. Syst. Signal Process..

[2]  B. Kamali,et al.  A novel direct sequence spread spectrum automatic vehicle identification system , 1996, Proceedings of Vehicular Technology Conference - VTC.

[3]  T. Takechi,et al.  Automobile identification based on the measurement of car sounds , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[4]  C. L. Nikias,et al.  Algorithms for Statistical Signal Processing , 2002 .

[5]  Khaled F. Hussain,et al.  Automatic vehicle classification system using range sensor , 2005, International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II.

[6]  Masanobu Abe,et al.  Waveform-based speech synthesis approach with a formant frequency modification , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Youlong Wu,et al.  Automatic vehicle classification instrument based on multiple sensor information fusion , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

[8]  Jinan Zhu,et al.  Vehicle identification based on self-organizing artificial neural network , 2000, IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. (Cat. No.00EX394).

[9]  Mel Siegel,et al.  Vehicle sound signature recognition by frequency vector principal component analysis , 1999, IEEE Trans. Instrum. Meas..

[10]  Kazuya Takeda,et al.  Synthesis of car noise based on a composition of engine noise and friction noise , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Hideki Kawahara,et al.  YIN, a fundamental frequency estimator for speech and music. , 2002, The Journal of the Acoustical Society of America.

[12]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..

[13]  R.A. Hauslen The promise of automatic vehicle identification , 1977, IEEE Transactions on Vehicular Technology.

[14]  A. Shirkhodaie,et al.  Vehicle identifications using acoustic sensing , 2007, Proceedings 2007 IEEE SoutheastCon.

[15]  Douglas E. Lake,et al.  Harmonic phase coupling for battlefield acoustic target identification , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[16]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[17]  Emanuele Pollastri,et al.  Musical Instrument Timbres Classification with Spectral Features , 2001, 2001 IEEE Fourth Workshop on Multimedia Signal Processing (Cat. No.01TH8564).

[18]  Manohar Das,et al.  An efficient technique for modeling and synthesis of automotive engine sounds , 2001, IEEE Trans. Ind. Electron..

[19]  H. R. Inhelder,et al.  Automatic vehicle identification systems—Methods of approach , 1970 .

[20]  D. Sheppard,et al.  Acoustic aircraft detection sensor , 1993, 1993 Proceedings of IEEE International Carnahan Conference on Security Technology.