Eduardo Zurek^1*, Margarita R. Gamarra², José R. Escorcia³, Carlos Gutierrez⁴, Henry Bayona⁵, Roxana P. Pérez⁶, and Xavier García⁷

¹ Universidad del Norte, Department of Systems Engineering, Barranquilla, Colombia. *Corresponding autor (e-mail: ezurek@uninorte.edu.co).

² Universidad Autónoma del Caribe, Department of Electronic and Telecommunication Engineering, IET group, Barranquilla, Colombia. (e-mail: margarita.gamarra@uac.edu.co).

³ Universidad Autónoma del Caribe, Department of Electronic and Telecommunication Engineering, IET group, Barranquilla, Colombia, and with the Escuela Naval de Suboficiales A.R.C. Barranquilla, Colombia. (e-mail: jescorcia@uac.edu.co).

⁴ Escuela Naval de Suboficiales A.R.C. Barranquilla, GITIN group, Barranquilla, Colombia. (e-mail: ing.cagm@gmail.com).

⁵ Escuela Naval de Suboficiales A.R.C. Barranquilla, GITIN group, Barranquilla, Colombia. (e-mail: elektronikesub@gmail.com).

⁷ Escuela Naval de Suboficiales A.R.C. Barranquilla, GITIN group, Barranquilla, Colombia. (e-mail: xavier.garciah@gmail.com).

Manuscript received on August 6, 2014
Accepted for publication on October 2, 2014
Published on November 15, 2014.

Abstract

This paper presents an implementation of Artificial Neural Networks (ANN) for acoustic fingerprints recognition, applied to the identification of marine vessels. In many cases, the vessel recognition process from an audible signal is performed by human operators, which could lead to failures in the identification process. Before entering the ANN classification process, the signal is filtered and its spectral characteristics are extracted. A comparison of the classification process between three types of neural networks is presented.

Key words: Acoustic fingerprint, FFT, PCA, ANN, feedforward backpropagation, RBF, PNN.

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