Abstract
In order to make effective recognition of radiated noises of ships, on the basis of the auditory Patterson-Holdsworth cochlear model and Meddis’ Inner Hair Cell (IHC) model, a feature extraction of radiated noises of ships model simulating the partial auditory system is set up to obtain the average firing rate. Then an algorithm (One-Against-All: OAA) of multi-class Support Vector Machines (SVMs) is defined. Finally, the extracted feature vectors are used to classify three different classes of targets using SVMs, BP Neural Network (BPNN) and K-Nearest Neighbor (KNN) methods. At the same time we compare the recognition performance of average firing rate feature with general power spectrum feature. Results show that the statistical recognition corrective rate of average firing rate feature exceeds 96.5% using SVMs.
This Paper was supported by NSFC (No. 60472108).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Patterson, R.D., Robinson, K., Holdsworth, J., et al.: Complex Sounds and Auditory Images. In: Auditory Physiology and Perceptionm, pp. 429–446 (1992)
Meddis, R.: Simulation of Mechanical to Neural Transduction in The Auditory Receptor. Journal of the Acoustical Society of America 79, 702–711 (1986)
Hewitt, M.J., Meddis, R.: Implementation Details of a Computation Model of the Inner Hair- Cell/Auditory-Nerve Synapse. Journal of the Acoustical Society of America 87, 1813–1816 (1990)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Wang, L.: Support Vector Machines: Theory and Applications. Springer, Heidelberg (2005)
Minh, N.N., Jagath, C.R.: Multi-Class Support Vector Machines for Protein Secondary Structure Prediction. Genome Informatics 14, 218–227 (2003)
Franc, V., Hlavac, V.: Multi-class Support Vector Machine. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 2, pp. 236–239 (2002)
Kang, C.Y., Zhang, X.H., Zhang, A.Q.: A Method of Passive Sonar Target Recognition Based on Spectrum Evaluation. Journal of Harbin Engineering University 24, 627–631 (2003)
Kang, C.Y., Zhang, X.H., Zhang, A.Q., Lin, H.W.: Underwater Acoustic Targets Classification Using Welch Spectrum Estimation and Neural Networks. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 930–935. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, X., Kang, C., Xia, Z. (2005). Recognition of Radiated Noises of Ships Using Auditory Features and Support Vector Machines. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_63
Download citation
DOI: https://doi.org/10.1007/11427445_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)