  Sound source localization is crucial in acoustic sensing and
monitoring-related applications. In this paper, we do a comprehensive analysis
of improvement in sound source localization by combining the direction of
arrivals (DOAs) with their derivatives which quantify the changes in the
positions of sources over time. This study uses the SALSA-Lite feature with a
convolutional recurrent neural network (CRNN) model for predicting DOAs and
their first-order derivatives. An update rule is introduced to combine the
predicted DOAs with the estimated derivatives to obtain the final DOAs. The
experimental validation is done using TAU-NIGENS Spatial Sound Events (TNSSE)
2021 dataset. We compare the performance of the networks predicting DOAs with
derivative vs. the one predicting only the DOAs at low SNR levels. The results
show that combining the derivatives with the DOAs improves the localization
accuracy of moving sources.
