A perceptually based approach for the improvement of automatic identification of naval targets

In this paper we investigate the identification of naval targets (ships or submarine) through the identification of the underwater sound they produce. Our approach is based on the use of Continuous Hidden Markov Models (CHMMs) to identify such naval targets. The general Gaussian density distribution HMM is developed for CHMM system. Several experiments have been conducted to study the effects of speed, distance and the direction of the naval targets on the identification rate (IR) of such targets using different features Mel-Frequency Cepstrum Coefficients (MFCCs), Perceptual Linear Prediction (PLP), and Relative Spectral PLP (RASTA-PLP). The obtained IR was found to be 100% (MFCCs & PLP) and 91.67 (RASTA) while changing the direction, 91.97% (MFCCs & PLP) and 83.33% (RASTA) while changing the distance and 58.3% (MFCCs & PLP) and 25% (RASTA) while changing the speed of the target. Results showed that speed has the maximum effect on the identification process. We applied our engine to 19 real targets signals representing 5 classes the results were 100% (MFCCs & PLP) and 84.2% (RASTA).

[1]  Abolghasem Sayadiyan,et al.  EVALUATION OF SOFT SEGMENT MODELING ON A CONTEXT INDEPENDENT PHONEME CLASSIFICATION SYSTEM , 2007 .

[2]  Hynek Hermansky,et al.  RASTA processing of speech , 1994, IEEE Trans. Speech Audio Process..

[3]  Maia E.M. Weddin.  Speaker Identification for Hearing Instruments , 2005 .

[4]  Qiang Huang,et al.  Underwater target classification using wavelet packets and neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Guy J. Brown,et al.  Auditory-Based Time-Frequency Representations and Feature Extraction Techniques for Sonar Processing , 2022 .

[6]  Shiwei Tang,et al.  Association rules enhanced classification of underwater acoustic signal , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[7]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[8]  Jiann-Der Lee,et al.  Classification of Underwater Signals Using Neural Networks , 2000 .

[9]  Robert W. Floyd,et al.  A sonar target recognition experiment , 1979 .

[10]  Jan Pool,et al.  Investigation of the impact of high frequency transmitted speech on speaker recognition , 2002 .

[11]  Chai Wutiwiwatchai,et al.  Comparative Study of Continuous Hidden Markov Models (CHMM) and Artificial Neural Network (ANN) on Speaker Identification System , 2001, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[12]  H.F. Hashem.  Automatic classification of underwater sonar signals , 2004, 7th Seminar on Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004.