Acoustic Signal Recognition Based on Time Series Model

Abstract:

Using time series model, isometric transformation time series model and ARTAFIT model, we deal with acoustic signal, obtaining different sets of parameters according to different acoustic signals. We use support vector machine (SVM) to recognize different acoustic signals by analyzing different sets of parameters. When the parameter set is too large, we should first reduce order making use of principal component analysis (PCA), then we can recognize them using support vector machine. In the end, we give a case study, which indicate the results of applying our models are satisfactory.

• Periodical: Advanced Materials Research (Volumes 189-193)
• Pages: 3243-3248
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.189-193.3243
• Online since: February 2011
• Authors: Yu Quan Cui, Le Jun Shi, Yu Wei Fang
• Keywords: ARMA Model, ARTAFIT Model, Signal Recognition, Support Vector Machine (SVM), Time Series Model

References

[1] Fan, J., Yao,Q. Nonlinear: Time Series[M]. Higher Education Press, 2005. 12.

[2] Deng, N., Tian,Y.: New Method For Data Mining—Support Vector Machine[M]. Science Press, 2004. 6.

[3] Bahar Biller, Barry L. Nelson.: Fitting Time-Series Input Processes for Simulation. Operations Research, 2005, 53(3): 549—559.

DOI: 10.1287/opre.1040.0190

[4] Bahar Biller, Barry L. Nelson.: Evaluation of the ARTAFIT Method for Fitting Time-Series Input Processes for Simulation. Informs Journal on Computing, 2008, 20(3): 485—498.

DOI: 10.1287/ijoc.1070.0261

[5] Bahar Biller, Barry L. Nelson.: Modeling and Generating Multivariate Time-Series Input Processes Using a Vector Autoregressive Technique. ACM Transactions on Modeling and Computer Simulation, 2003, 13(3): 211—237.

DOI: 10.1145/937332.937333

[6] Bahar Biller, Barry L. Nelson.: Parameter Estimation for ARTA Processes. Proceedings of the 2002 Winter Simulation Conference: 255—262.

DOI: 10.1109/wsc.2002.1172893

[7] Bahar Biller, Barry L. Nelson.: Online Companion to Fitting Time-Series Input Processes for Simulation,. (2005).

[8] Zhang,G., Jin,W., Hu,L. : Based on rough set theory Radar Emitter Signal Recognition[J]. Journal of Xi'an Jiaotong University, 2005, 39(8): 871—875.

[9] Zhang,Z., Guan,X., Li,Y.: Based on rough set and RBF neural network recognition of radar emitter signal[J]. Aeronautical Computer Technique, 2009, 39(4): 18—21.

[10] Wang,Y., Zhou,C., Huang,Y., Feng,X.: Based on Minimal Uncertainty Neural Networks Taste Identification of Tea[J]. Journal of Computer Research and Development, 2005, 42(1): 66—71.

DOI: 10.1360/crad20050109

[11] Chen,J., Kuo,Y., Li,J., Ma,Y.: Based on Wavelet Transform Digital Modulation Recognition Method[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2026—(2029).

[12] Wang,Y., Wang,J., Len,Y.: The Ratio of Wavelet algorithm and its application [J]. Chinese Journal of Engineering Geophysics, 2004, 1(5): 404—406.

[13] Tu,Y., Li,J., Wang,G., Li,R.: The Radar Emitter Signal Recognition Based on Welch Power Spectrum[J]. Aerospace Electronic Warfare, 2009, 25(3): 32—34.

[14] Zou,X., Zhang,G., Li,M., Li, X.: A New Method of radar emitter signal classification[J]。Journal of Data Acquisition & Processing, 2009, 24(4): 487—492.

[15] C.W.J. Granger, T. Teräsvirta.: Modelling Nonlinear Economic Relationships[M]. Shanghai University of Finance and Economics Press, 2006. 6.