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In this paper, we propose a novel four-stage data augmentation approach toResNet-Conformer based acoustic modeling for sound event localization anddetection (SELD). First, we explore two spatial augmentation techniques, namelyaudio channel swapping (ACS) and multi-channel simulation (MCS), to deal withdata sparsity in SELD. ACS and MDS focus on augmenting the limited trainingdata with expanding direction of arrival (DOA) representations such that theacoustic models trained with the augmented data are robust to localizationvariations of acoustic sources. Next, time-domain mixing (TDM) andtime-frequency masking (TFM) are also investigated to deal with overlappingsound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined ina step-by-step manner to form an effective four-stage data augmentation scheme.Tested on the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 data set, our proposed augmentation approach greatly improves thesystem performance, ranking our submitted system in the first place in the SELDtask of the DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformerarchitecture to model both global and local context dependencies of an audiosequence and win the first place in the DCASE 2022 SELD evaluations.

A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection