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Sound event localization and detection consists of two subtasks which aresound event detection and direction-of-arrival estimation. While sound eventdetection mainly relies on time-frequency patterns to distinguish differentsound classes, direction-of-arrival estimation uses magnitude or phasedifferences between microphones to estimate source directions. Therefore, it isoften difficult to jointly train these two subtasks simultaneously. We proposea novel feature called spatial cue-augmented log-spectrogram (SALSA) with exacttime-frequency mapping between the signal power and the sourcedirection-of-arrival. The feature includes multichannel log-spectrogramsstacked along with the estimated direct-to-reverberant ratio and a normalizedversion of the principal eigenvector of the spatial covariance matrix at eachtime-frequency bin on the spectrograms. Experimental results on the DCASE 2021dataset for sound event localization and detection with directionalinterference showed that the deep learning-based models trained on this newfeature outperformed the DCASE challenge baseline by a large margin. Wecombined several models with slightly different architectures that were trainedon the new feature to further improve the system performances for the DCASEsound event localization and detection challenge.

DCASE 2021 Task 3: Spectrotemporally-aligned Features for Polyphonic Sound Event Localization and Detection