In this paper, we present some preliminary results using the Speech Enhancement Generative Adversarial Network (SEGAN) for the attenuation of the ego-noise in the speech source localization problem embedded in unmanned aerial vehicles (UAV). This task is of great interest in UAV search and rescue scenarios. The primary motivation of using the SEGAN is that it seems to preserve the waveform of the speech signal, which is essential for time-based direction of arrival (TDOA) algorithms. Although preliminary, the obtained results open an excellent perspective for its usage in this problem and despite its computational burden in the training stage, once the SEGAN is trained, it can be implemented for working in real-time scenarios.