# The Long-Range Biomimetic Covert Communication Method Mimicking Large Whale
## 4 Evaluation of Degree-of-Mimic Performance  
This section evaluates the degree-of-mimic (DoM) performance of the proposed method. Quantitative (i.e., spectral correlation) and qualitative methods (i.e., MOS-test) have been proposed to measure the degree of mimic in conventional biomimetic studies [8; 9; 10; 11; 12; 13; 14]. Spectral correlation measures the similarity of the time-frequency change between the actual whale whistle, and the MOS test evaluates whether a person recognizes a mimicking whistle as an actual whale whistle. The results of conventional studies show that the biomimicking whistle has the best score in both DOM evaluation methods (i.e., spectral correlation and MOS test) when it is not distorted during the modulation process [10; 11]. Since the proposed method does not distort the whale whistle, it has the highest score of the two methods and is undetected by conventional communication signal-recognition techniques, which find the artificial and periodical signal characteristics. Therefore, this paper proposes a novel DoM evaluation method to more precisely evaluate the DoM of biomimicking signals.  
The proposed DoM evaluation method discriminates even if the biomimicking whistle is the sound of a specific whale species. The proposed method not only judges whether a biomimicking whistle sounds like a whale, but also determines whether it simply sounds like a specific species (e.g., right humpback, killer, etc.). Determining a species of whale with only whistles is a professional task and requires much experience. Thus, to complete this task effectively, whistle classifiers based on machine learning have been studied [39; 40]. In this paper, the machine-learning classifier was utilized to evaluate the DoM performance of the proposed method. The classifier that won the Marine Explore and Cornell University Whale Detection Challenge competition was used for evaluation [38]. To evaluate the DoM, 1000 biomimicking whistles were generated using the model in Section 3. These 1000 whistles were modulated with conventional methods (i.e., CV-CFM and TFSK [10; 13] and FSK [6; 7; 8]) to compare the DoM performance of the proposed method with that of conventional methods. TFSK-modulated biomimicking whistles are mathematically the same signal as CV-CFM-modulated whistles [13]. Thus, these two modulation schemes shared whistles for evaluation. Note that all models with six window lengths were utilized to generate 1000 whistles. Figure 8 shows examples of the whistles generated for the DoM evaluation.  
The whistle in Figure 8 was generated by the model with a 0.2 s window length. The results of the DoM evaluation are shown in Table 1.  
In Table 1, 95% of whistles generated by the proposed method were recognized as the whistle of the RHW. Note that the classifier did not simply identify the given sound as an actual whistle of a whale but rather as the whistle of the RHW. Thus, this result demonstrates that the proposed model generated a whistle like the real RHW whistle. However, in cases of FSK- and CV-CFM-modulated whistles, only 10% and 60% were recognized as the whistle of the RHW, respectively. Note that these three types of whistles have the same frequency contour; the only difference is the modulation scheme. Therefore, this result shows that the conventional methods distorted the whistle when modulating bits. This distortion decreased the DoM performance. In addition, as seen in Figure 8b,c, the existing method distorted the signal so humans could recognize this difference and the MOS score decreased. The value of spectral correlation also decreased. As a result of the DoM evaluation, the DoM performance of the proposed method is superior to that of the  
Figure 7: Block diagram of the proposed biomimicking demodulation.  
conventional methods. In the next section, the computer simulations and ocean-experiment results are shown for communication-performance comparisons of the proposed method with the conventional methods.