# WhistleGAN for Biomimetic Underwater Acoustic Covert Communication
## 1 Introduction  
Underwater acoustic (UWA) covert communication requires a low probability of detection/interception (LPD/LPI). The conventional direct sequence spread spectrum (DSSS) method, which multiplies a communication signal by a frequency band spread code, has been widely used as a covert communication method. In UWA covert communications, however, the narrow usable bandwidth limits the large spreading factors in DSSS, and a low transmitting power reduces the communication range [1; 2; 3; 4; 5; 6; 7]. To solve this problem, researchers have studied the biomimetic UWA covert communication method that mimics and transmits the sound of marine creatures to confuse the enemy [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. The biomimetic UWA covert communication mainly transmits communication signals by mimicking dolphin whistles, which propagate longer than dolphin clicks [16; 17]. The conventional biomimetic UWA covert communication utilizes previously recorded dolphin whistles for communication signals. Continuous varying carrier frequency modulation (CV-CFM) in [16] divided the whistles into several slots in the time domain and allocated bits to the slots, and time frequency shift keying (TFSK) in [8] shifted the original whistles in time and frequency [8; 16; 17]. The combined method of CV-CFM and TFSK for transmitting bits over overlapped whistles was also investigated [17]. Since these conventional methods utilized a limited number of recorded dolphin data sets, the same data were repeatedly transmitted and the LPD/LPI performance of the biomimetic UWA covert communications decreased. In general, two methods are used to obtain the dolphin whistles for the biomimetic UWA covert communication: one method records the real dolphin whistles by themselves and the other utilizes the published data set recorded by others. However, recording dolphins' whistles is challenging due to their movement, which can cover hundreds ofkilometers [18; 19], and the quantity of published data is limited because dolphin whistles contain important marine environment information of some countries. This paper proposes a whistle-generative adversarial network (WhistleGAN) with a Mel filter bank to create a new data set of dolphin whistles. The goal was to create new whistles that mimic the sounds of actual whistles to human listeners, while the created whistles do not have the exact same frequency contour as the recorded whistles. Many generative adversarial networks (GAN) such as deep convolutional generative adversarial networks (DCGAN), progressive growing of GAN (ProGAN), large-scale GAN (BigGAN), and VQGAN+CLIP have been researched and present superior generation performance [20; 21; 22; 23]. DCGAN produces high-quality images even with limited computing resources and datasets, however, it is not sufficiently good to generate high-resolution images [20]. ProGAN generates high-resolution images, yet the training process of ProGAN is complex and requires substantial computing resources [21]. BigGAN creates high-quality images through large datasets, however, it demands considerable computing time and resources for training [22]. VQGAN+CLIP generates images of various styles and resolutions, however, it has the drawback of requiring significant time and resources to train the VQGAN model [23]. In practice, acquiring a substantial database of recorded actual dolphin whistle sounds is very difficult. The purpose of this paper is to generate dolphin whistles using a low-complexity method using a relatively small amount of actual recorded dolphin whistle data. Therefore, we chose the DCGAN model for the proposed method. The whistles generated by the proposed method provide a low computational complexity while keeping a high mimicking performance. The proposed WhistleGAN uses the Mel filter bank to convert linear frequency bands to a Mel scale because the frequency response of the human ear is perceived as octaves and lower frequencies are detected more sensitively than higher frequencies.