  Sound event localization frameworks based on deep neural networks have shown
increased robustness with respect to reverberation and noise in comparison to
classical parametric approaches. In particular, recurrent architectures that
incorporate temporal context into the estimation process seem to be well-suited
for this task. This paper proposes a novel approach to sound event localization
by utilizing an attention-based sequence-to-sequence model. These types of
models have been successfully applied to problems in natural language
processing and automatic speech recognition. In this work, a multi-channel
audio signal is encoded to a latent representation, which is subsequently
decoded to a sequence of estimated directions-of-arrival. Herein, attentions
allow for capturing temporal dependencies in the audio signal by focusing on
specific frames that are relevant for estimating the activity and
direction-of-arrival of sound events at the current time-step. The framework is
evaluated on three publicly available datasets for sound event localization. It
yields superior localization performance compared to state-of-the-art methods
in both anechoic and reverberant conditions.
