  Sound event localization and detection (SELD) involves sound event detection
(SED) and direction of arrival (DoA) estimation tasks. SED mainly relies on
temporal dependencies to distinguish different sound classes, while DoA
estimation depends on spatial correlations to estimate source directions. This
paper addresses the need to simultaneously extract spatial-temporal information
in audio signals to improve SELD performance. A novel block, the sliding-window
graph-former (SwG-former), is designed to learn temporal context information of
sound events based on their spatial correlations. The SwG-former block
transforms audio signals into a graph representation and constructs graph
vertices to capture higher abstraction levels for spatial correlations. It uses
different-sized sliding windows to adapt various sound event durations and
aggregates temporal features with similar spatial information while
incorporating multi-head self-attention (MHSA) to model global information.
Furthermore, as the cornerstone of message passing, a robust Conv2dAgg function
is proposed and embedded into the block to aggregate the features of neighbor
vertices. As a result, a SwG-former model, which stacks the SwG-former blocks,
demonstrates superior performance compared to recent advanced SELD models. The
SwG-former block is also integrated into the event-independent network version
2 (EINV2), called SwG-EINV2, which surpasses the state-of-the-art (SOTA)
methods under the same acoustic environment.
