  Our goal is to develop a sound event localization and detection (SELD) system
that works robustly in unknown environments. A SELD system trained on known
environment data is degraded in an unknown environment due to environmental
effects such as reverberation and noise not contained in the training data.
Previous studies on related tasks have shown that domain adaptation methods are
effective when data on the environment in which the system will be used is
available even without labels. However adaptation to unknown environments
remains a difficult task. In this study, we propose echo-aware feature
refinement (EAR) for SELD, which suppresses environmental effects at the
feature level by using additional spatial cues of the unknown environment
obtained through measuring acoustic echoes. FOA-MEIR, an impulse response
dataset containing over 100 environments, was recorded to validate the proposed
method. Experiments on FOA-MEIR show that the EAR effectively improves SELD
performance in unknown environments.
