  Sound event localization and detection consists of two subtasks which are
sound event detection and direction-of-arrival estimation. While sound event
detection mainly relies on time-frequency patterns to distinguish different
sound classes, direction-of-arrival estimation uses magnitude or phase
differences between microphones to estimate source directions. Therefore, it is
often difficult to jointly train these two subtasks simultaneously. We propose
a novel feature called spatial cue-augmented log-spectrogram (SALSA) with exact
time-frequency mapping between the signal power and the source
direction-of-arrival. The feature includes multichannel log-spectrograms
stacked along with the estimated direct-to-reverberant ratio and a normalized
version of the principal eigenvector of the spatial covariance matrix at each
time-frequency bin on the spectrograms. Experimental results on the DCASE 2021
dataset for sound event localization and detection with directional
interference showed that the deep learning-based models trained on this new
feature outperformed the DCASE challenge baseline by a large margin. We
combined several models with slightly different architectures that were trained
on the new feature to further improve the system performances for the DCASE
sound event localization and detection challenge.
