This work focuses on the evaluation of the impact of the Multi-Head Attention algorithm in the field of Music Source Separation. In particular, our objective is to determine its potential to outperform the U-Net architecture often employed in state-of-the-art (SOTA) models. Additional primary goals include examining the repercussions of integrating Self-Supervised features into the pipeline and assessing the efficacy of the Attention mechanism for phase estimation. Notably, when utilizing the magnitude spectrogram as input, our model demonstrated promising outcomes, especially when using an increased volume of training data. The incorporation of Self-Supervised features into the model's architecture proved to be effective only when all layer representations are combined into a weighted sum.