  Polyphonic sound event localization and detection (SELD), which jointly
performs sound event detection (SED) and direction-of-arrival (DoA) estimation,
detects the type and occurrence time of sound events as well as their
corresponding DoA angles simultaneously. We study the SELD task from a
multi-task learning perspective. Two open problems are addressed in this paper.
Firstly, to detect overlapping sound events of the same type but with different
DoAs, we propose to use a trackwise output format and solve the accompanying
track permutation problem with permutation-invariant training. Multi-head
self-attention is further used to separate tracks. Secondly, a previous finding
is that, by using hard parameter-sharing, SELD suffers from a performance loss
compared with learning the subtasks separately. This is solved by a soft
parameter-sharing scheme. We term the proposed method as Event Independent
Network V2 (EINV2), which is an improved version of our previously-proposed
method and an end-to-end network for SELD. We show that our proposed EINV2 for
joint SED and DoA estimation outperforms previous methods by a large margin,
and has comparable performance to state-of-the-art ensemble models.
