Dynamic Kernel Convolution Network with Scene-dedicate Training for Sound Event Localization and Detection
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationSound Event Localization and DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Constrained Deformable Convolutional Network for Efficient Single Image Dynamic Scene Blind Deblurring with Spatially-Variant Motion Blur Kernels Estimation
Most existing deep-learning-based single image dynamic scene blind deblurring (SIDSBD) methods usually design deep networks to directly remove the spatially-variant motion blurs from one inputted motion blurred image, wi…
DeblurringImage RestorationQDC: Quantum Diffusion Convolution Kernels on Graphs
Graph convolutional neural networks (GCNs) operate by aggregating messages over local neighborhoods given the prediction task under interest. Many GCNs can be understood as a form of generalized diffusion of input featur…
DISK: Differentiable Sparse Kernel Complex for Efficient Spatially-Variant Convolution
Image convolution with complex kernels is a fundamental operation in photography, scientific imaging, and animation effects, yet direct dense convolution is computationally prohibitive on resource-limited devices. Existi…
Attention-based Dynamic Graph Convolutional Recurrent Neural Network for Traffic Flow Prediction in Highway Transportation
As one of the important tools for spatial feature extraction, graph convolution has been applied in a wide range of fields such as traffic flow prediction. However, current popular works of graph convolution cannot guara…
Kernel Adaptive Convolution for Scene Text Detection via Distance Map Prediction
Segmentation-based scene text detection algorithms that are accurate to the pixel level can satisfy the detection of arbitrary shape scene text and have received widespread attention. On the one hand due to the compl…
Foreground SegmentationScene Text DetectionText Detection