  The interest in deep learning methods for solving traditional signal
processing tasks has been steadily growing in the last years. Time delay
estimation (TDE) in adverse scenarios is a challenging problem, where classical
approaches based on generalized cross-correlations (GCCs) have been widely used
for decades. Recently, the frequency-sliding GCC (FS-GCC) was proposed as a
novel technique for TDE based on a sub-band analysis of the cross-power
spectrum phase, providing a structured two-dimensional representation of the
time delay information contained across different frequency bands. Inspired by
deep-learning-based image denoising solutions, we propose in this paper the use
of convolutional neural networks (CNNs) to learn the time-delay patterns
contained in FS-GCCs extracted in adverse acoustic conditions. Our experiments
confirm that the proposed approach provides excellent TDE performance while
being able to generalize to different room and sensor setups.
