  Sound event localization aims at estimating the positions of sound sources in
the environment with respect to an acoustic receiver (e.g. a microphone array).
Recent advances in this domain most prominently focused on utilizing deep
recurrent neural networks. Inspired by the success of transformer architectures
as a suitable alternative to classical recurrent neural networks, this paper
introduces a novel transformer-based sound event localization framework, where
temporal dependencies in the received multi-channel audio signals are captured
via self-attention mechanisms. Additionally, the estimated sound event
positions are represented as multivariate Gaussian variables, yielding an
additional notion of uncertainty, which many previously proposed deep
learning-based systems designed for this application do not provide. The
framework is evaluated on three publicly available multi-source sound event
localization datasets and compared against state-of-the-art methods in terms of
localization error and event detection accuracy. It outperforms all competing
systems on all datasets with statistical significant differences in
performance.
