  Deep learning models are widely applied in the signal processing community,
yet their inner working procedure is often treated as a black box. In this
paper, we investigate the use of eXplainable Artificial Intelligence (XAI)
techniques to learning-based end-to-end speech source localization models. We
consider the Layer-wise Relevance Propagation (LRP) technique, which aims to
determine which parts of the input are more important for the output
prediction. Using LRP we analyze two state-of-the-art models, of differing
architectural complexity that map audio signals acquired by the microphones to
the cartesian coordinates of the source. Specifically, we inspect the relevance
associated with the input features of the two models and discover that both
networks denoise and de-reverberate the microphone signals to compute more
accurate statistical correlations between them and consequently localize the
sources. To further demonstrate this fact, we estimate the Time-Difference of
Arrivals (TDoAs) via the Generalized Cross Correlation with Phase Transform
(GCC-PHAT) using both microphone signals and relevance signals extracted from
the two networks and show that through the latter we obtain more accurate
time-delay estimation results.
