  Sound event detection (SED) and localization refer to recognizing sound
events and estimating their spatial and temporal locations. Using neural
networks has become the prevailing method for SED. In the area of sound
localization, which is usually performed by estimating the direction of arrival
(DOA), learning-based methods have recently been developed. In this paper, it
is experimentally shown that the trained SED model is able to contribute to the
direction of arrival estimation (DOAE). However, joint training of SED and DOAE
degrades the performance of both. Based on these results, a two-stage
polyphonic sound event detection and localization method is proposed. The
method learns SED first, after which the learned feature layers are transferred
for DOAE. It then uses the SED ground truth as a mask to train DOAE. The
proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains
different overlapping sound events in different environments. Experimental
results show that the proposed method is able to improve the performance of
both SED and DOAE, and also performs significantly better than the baseline
method.
