Blind Source Separation (BSS) has always been an active research field within the signal processing community; it is used to reconstruct primary source signals from their observed mixtures. Independent Component Analysis has been and is still used to solve the BSS problem; however, it is based on the mutual independence of the original source signals. In this paper, we propose to use Copulas to model the dependency structure between these signals, enabling the separation of dependent source components; we also deploy \(\alpha \)-divergence as our cost function to minimize, considering its superiority to handle noisy data as well as its ability to converge faster. We test our approach for various values of alpha and give a comparative study between the proposed methodology and other existing methods; this approach exhibited a higher quality performance and accuracy, especially when the value of \(\alpha \) is equal to \(\frac{1}{2}\), which is equivalent to the Hellinger divergence.

盲源分离（BSS）一直是信号处理社区中一个活跃的研究领域。它用于从观察到的混合信号中重建主要源信号。独立分量分析已经并且仍然用于解决BSS问题。但是，它基于原始源信号的相互独立性。在本文中，我们建议使用Copulas对这些信号之间的依赖性结构进行建模，从而实现对依赖性源成分的分离。我们也部署\（\ alpha \）-差异是我们将成本函数最小化的原因，要考虑到它在处理嘈杂数据方面的优势以及更快收敛的能力。我们测试了各种alpha值的方法，并在建议的方法和其他现有方法之间进行了比较研究；这种方法表现出更高的质量性能和准确性，尤其是当\（\ alpha \）的值等于\（\ frac {1} {2} \）时，这相当于Hellinger散度。