The process of separating music and speech signals can be categorized into three scenarios. The first scenario occurs when the signals overlap only in the time domain, while the second scenario takes place when they intersect exclusively in the frequency domain. The third scenario, which is the more complex case, arises when they overlap in both time and frequency domains. To address the first scenario, a specialized frequency-domain filter is employed to distinguish the signals. When overlap occurs in the frequency domain, a time-domain filter can be utilized for segregation. However, when signals overlap in both domains, the challenge becomes more intricate, necessitating additional constraints for an effective solution. In this paper, an evolutionary voting algorithm is introduced to resolve the first two cases through enhancements of the ZCR and STE techniques, while another approach utilizing ICA is explored to tackle the third scenario with specific constraints. The paper also presents theoretical analyses and simulation results to evaluate the performance of the proposed algorithm.