  Localizing a moving sound source in the real world involves determining its
direction-of-arrival (DOA) and distance relative to a microphone. Advancements
in DOA estimation have been facilitated by data-driven methods optimized with
large open-source datasets with microphone array recordings in diverse
environments. In contrast, estimating a sound source's distance remains
understudied. Existing approaches assume recordings by non-coincident
microphones to use methods that are susceptible to differences in room
reverberation. We present a CRNN able to estimate the distance of moving sound
sources across multiple datasets featuring diverse rooms, outperforming a
recently-published approach. We also characterize our model's performance as a
function of sound source distance and different training losses. This analysis
reveals optimal training using a loss that weighs model errors as an inverse
function of the sound source true distance. Our study is the first to
demonstrate that sound source distance estimation can be performed across
diverse acoustic conditions using deep learning.
