  Polyphonic sound event localization and detection is not only detecting what
sound events are happening but localizing corresponding sound sources. This
series of tasks was first introduced in DCASE 2019 Task 3. In 2020, the sound
event localization and detection task introduces additional challenges in
moving sound sources and overlapping-event cases, which include two events of
the same type with two different direction-of-arrival (DoA) angles. In this
paper, a novel event-independent network for polyphonic sound event
localization and detection is proposed. Unlike the two-stage method we proposed
in DCASE 2019 Task 3, this new network is fully end-to-end. Inputs to the
network are first-order Ambisonics (FOA) time-domain signals, which are then
fed into a 1-D convolutional layer to extract acoustic features. The network is
then split into two parallel branches. The first branch is for sound event
detection (SED), and the second branch is for DoA estimation. There are three
types of predictions from the network, SED predictions, DoA predictions, and
event activity detection (EAD) predictions that are used to combine the SED and
DoA features for on-set and off-set estimation. All of these predictions have
the format of two tracks indicating that there are at most two overlapping
events. Within each track, there could be at most one event happening. This
architecture introduces a problem of track permutation. To address this
problem, a frame-level permutation invariant training method is used.
Experimental results show that the proposed method can detect polyphonic sound
events and their corresponding DoAs. Its performance on the Task 3 dataset is
greatly increased as compared with that of the baseline method.
