  DNN-based methods have shown high performance in sound event localization and
detection(SELD). While in real spatial sound scenes, reverberation and the
imbalanced presence of various sound events increase the complexity of the SELD
task. In this paper, we propose an effective SELD system in real spatial
scenes.In our approach, a dynamic kernel convolution module is introduced after
the convolution blocks to adaptively model the channel-wise features with
different receptive fields. Secondly, we incorporate the SELDnet and EINv2
framework into the proposed SELD system with multi-track ACCDOA. Moreover, two
scene-dedicated strategies are introduced into the training stage to improve
the generalization of the system in realistic spatial sound scenes. Finally, we
apply data augmentation methods to extend the dataset using channel rotation,
spatial data synthesis. Four joint metrics are used to evaluate the performance
of the SELD system on the Sony-TAu Realistic Spatial Soundscapes 2022
dataset.Experimental results show that the proposed systems outperform the
fixed-kernel convolution SELD systems. In addition, the proposed system
achieved an SELD score of 0.348 in the DCASE SELD task and surpassed the SOTA
methods.
