Neural Network Approach to Acoustic Detection of Number of Vehicles
The paper reports results of applying a perceptron neural network to determine the number of sound sources on a scene monitored by an array of linearly spaced microphones. The standard techniques for solving this problem were found inadequate in the presence of normal disturbances (such as produced by wind). The paper proposes an indirect application of a perceptron neural network, to analyze the results of the MUSIC beam forming technique. The method is experimentally shown to deal with this problem. Field experiments included scenes with zero, one or two moving vehicles proved the system effectiveness.
KeywordsSound Source Neural Network Approach Signal Subspace Short Time Fourier Transform Microphone Array
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- 1.Bishop Ch. (1995) Neural Networks for Pattern Recognition. Clarendon Press, OxfordGoogle Scholar
- 2.Blesler Y., Macovski A. (1986) Exact Maximum Likelihood Parameter Estimation of Superimposed Exponential Signals in Noise. IEEE Trans. on Acoustics, Speech and Signal Processing 34, 1081–1089Google Scholar
- 3.Caban D., Walkowiak T. (1999) Application of Neural Networks to Determine the Number of Acoustic Sources. 5`h International Conference on Soft Computing, Mendel ‘89, Brno, Czech Republic, June 9–12, 329–332Google Scholar
- 4.Johnson, D.H., Dudgeon D.E. (1993) Array Signal Processing: Concepts and Techniques. Prentice Hall, Englewood Clifs, New YorkGoogle Scholar
- 5.Luo F.L., Unbehauen R. (1998) Applied Neural Networks for Signal Processing. Cambridge University PressGoogle Scholar
- 6.Moulines E., Cardoso J-F. (1991) Second-order versus fourth-order MUSIC algorithms: an asymptotical statistical analysis. Int. Sig. Proc. Workshop on Higher Order Statistics, Chamrouse, FranceGoogle Scholar
- 7.Schmidt R.O. (1979) Multiple emitter location and signal parameter estimation. Proc. RADC Spectrum Estimation Workshop, Rome, 243–258Google Scholar
- 8.Smith S. W. (1999) The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical PublishingGoogle Scholar
- 9.Wang H., Kaveh K. (1999) Coherent Signal-Subspace Processing for the Detection and Estimation of Angles of Arrival of Multiple Wide-Band Sources. IEEE Trans. on Acoustics, Speech and Signal Processing 33, 823–831Google Scholar
- 10.Zamoj ski W. et al. (1999) A System for Recognition and Tracking of Moving Objects on the Basis of Acoustic Information. Reports of the Institute of Engineering Cybernetics, Wroclaw University of Technology, PRE 17/99, Wroclaw, Poland (In Polish)Google Scholar