  Polyphonic sound event detection and localization (SELD) task is challenging
because it is difficult to jointly optimize sound event detection (SED) and
direction-of-arrival (DOA) estimation in the same network. We propose a general
network architecture for SELD in which the SELD network comprises sub-networks
that are pretrained to solve SED and DOA estimation independently, and a
recurrent layer that combines the SED and DOA estimation outputs into SELD
outputs. The recurrent layer does the alignment between the sound classes and
DOAs of sound events while being unaware of how these outputs are produced by
the upstream SED and DOA estimation algorithms. This simple network
architecture is compatible with different existing SED and DOA estimation
algorithms. It is highly practical since the sub-networks can be improved
independently. The experimental results using the DCASE 2020 SELD dataset show
that the performances of our proposed network architecture using different SED
and DOA estimation algorithms and different audio formats are competitive with
other state-of-the-art SELD algorithms. The source code for the proposed SELD
network architecture is available at Github.

Audio and Speech Processing