Global Journal of Human-Social Science, B: Geography, Environmental Science and Disaster Management, Volume 22 Issue 3

Figure 2: The Similarity Coefficient Difference Comparing the difference in the Similarity Coefficient in two experiments at a distance between two microphones, the result is shown in Figure 2. The difference approximates 0, which indicates the two degrees of similarity are almost the same, then is significant, and lastly goes back to be near-zero value. It suggests an interval of distance in which the Similarity Coefficient is significantly different for one source against three sources. This case agrees with Similarity Principle Ⅱ . A suitable distance (0.2m) between two microphones can be found, where the Similarity Coefficient is high in one source but low in three sources. According to the principle, it is possible to judge whether there is only one source or two more by making two receivers be arranged at a proper distance. V. D iscussion This study shows a physical principle, the similarity principle, verified by acoustical experiments. In the traditional sense, waves emanated from the same source should be highly similar, and similarity should be little related to the distance between two receivers. However, Similarity Principle Ⅰ negates this traditional sense. Similarity Principle Ⅱ suggests that the distance between man’s two ears should result from evolution. Such a distance is proper for a man to judge whether the sounds come from the same source or not. R eferences R éférences R eferencias 1. Romano, J. D., & Cornish, N. (2017). Detection methods for stochastic gravitational-wave backgrounds: a unified treatment. Living reviews in relativity, 20(1), 1-223. 2. Sathyaprakash, B. S. (2013). Gravitational waves and astrophysical sources. Comptes Rendus Physique, 14(4), 272-287. 3. Cheng, W., Liu, L., & Wang, G. (2021). A new method for estimating the correlation of seismic waveforms based on the NTFT. Geophysical Journal International, 226(1), 368-376. 4. Asuero, A. G., Sayago, A., & Gonzalez, A. G. (2006). The correlation coefficient: An overview. Critical reviews in analytical chemistry, 36(1), 41-59. 5. Tan, M., Yuan, S. P., & Su, Y. X. (2017). A learning- based approach to text image retrieval: using CNN features and improved similarity metrics. CoRR. 6. Xu, H., & Deng, Y. (2017). Dependent evidence combination based on shearman coefficient and pearson coefficient. IEEE Access, 6, 11634-11640. 7. Ebru Temizhan,Hamit Mirtagioglu & Mehmet Mendes. (2022). Which Correlation Coefficient Should Be Used for Investigating Relations between Quantitative Variables?. American Academic Scientific Research Journal for Engineering, Technology, and Sciences(1). 8. Linhai Jing, Q. C., & Panahi, A. (2006). Principal component analysis with optimum order sample correlation coefficient for image enhancement. International Journal of Remote Sensing, 27(16), 3387-3401. 9. Ronny Vallejos, Javier Pérez, Aaron M. Ellison & Andrew D. Richardson. (2019). A spatial concordance correlation coefficient with an application to image analysis. Spatial Statistics. doi: 10.1016/j.spasta.2019.100405. 10. Meghanathan, N. (2015, April). Correlation coefficient analysis of centrality metrics for complex network graphs. In Computer Science On-line Conference (pp. 11-20). Springer, Cham. 11. Sun, Zhen & Wang, Guocheng & Su, Xiaoqing & Liang, Xinghui & Liu, Lintao. (2020). Similarity and delay between two non-narrow-band time signals. 12. Su, X., Liu, L., Houtse, H., & Wang, G. (2014). Long- term polar motion prediction using normal time– frequency transform. Journal of Geodesy, 88(2), 145-155. 13. Liu, L., & Hsu, H. (2011, July). Inversion and normalization of time-frequency transform. In 2011 international conference on multimedia technology (pp. 2164-2168). IEEE. © 2022 Global Journals Volume XXII Issue III Version I 53 ( ) Global Journal of Human Social Science - Year 2022 B Similarity Principle and its Acoustical Verification