  Sound event localization and detection (SELD) combines the identification of
sound events with the corresponding directions of arrival (DOA). Recently,
event-oriented track output formats have been adopted to solve this problem;
however, they still have limited generalization toward real-world problems in
an unknown polyphony environment. To address the issue, we proposed an
angular-distance-based multiple SELD (AD-YOLO), which is an adaptation of the
"You Only Look Once" algorithm for SELD. The AD-YOLO format allows the model to
learn sound occurrences location-sensitively by assigning class responsibility
to DOA predictions. Hence, the format enables the model to handle the polyphony
problem, regardless of the number of sound overlaps. We evaluated AD-YOLO on
DCASE 2020-2022 challenge Task 3 datasets using four SELD objective metrics.
The experimental results show that AD-YOLO achieved outstanding performance
overall and also accomplished robustness in class-homogeneous polyphony
environments.
