WhistleGAN for Biomimetic Underwater Acoustic Covert Communication

Abstract

This paper proposes a whistle-generative adversarial network (WhistleGAN) that generates whistles for biomimetic underwater covert acoustic communication. The proposed method generates new whistles to maintain covertness by avoiding the repetitive use of the same whistles. Since the human ear perceives octave frequency such that low-frequency resolution is relatively larger than that of low frequencies, the proposed WhistleGAN uses mel filter banks to keep the fidelity in mimicking while reducing the complexity. The mean opinion score test verified that the whistles generated by the proposed method and the recorded real whistles have a similar score of 4.3, and the computer simulations proved that the bit error rate performance of the proposed method is the same as that of the real whistle.