  The understanding of the surrounding environment plays a critical role in
autonomous robotic systems, such as self-driving cars. Extensive research has
been carried out concerning visual perception. Yet, to obtain a more complete
perception of the environment, autonomous systems of the future should also
take acoustic information into account. Recent sound event localization and
detection (SELD) frameworks utilize convolutional recurrent neural networks
(CRNNs). However, considering the recurrent nature of CRNNs, it becomes
challenging to implement them efficiently on embedded hardware. Not only are
their computations strenuous to parallelize, but they also require high memory
bandwidth and large memory buffers. In this work, we develop a more robust and
hardware-friendly novel architecture based on a temporal convolutional
network(TCN). The proposed framework (SELD-TCN) outperforms the
state-of-the-art SELDnet performance on four different datasets. Moreover,
SELD-TCN achieves 4x faster training time per epoch and 40x faster inference
time on an ordinary graphics processing unit (GPU).
