Sound event localization and detection network with enhanced feature expression

To address the problem that traditional deep learning models are difficult to capture the long-context feature correlations in input feature maps as well as the key feature information in channel and spatial dimensions, resulting in high error rates and unsatisfactory performance in sound event localization and detection (SELD). Based on the baseline model SELDnet in the acoustic scene classification and sound event detection challenge, this paper proposes a feature enhanced sound event localization and detection network (FE-SELDnet). In order to address the issue of function failure to backpropagate, which leads to neuron death, it suggests using group normalization and the SiLU activation function; introducing the convolutional block attention module (CBAM) to capture significant features in both channel and spatial dimensions of acoustic features, suppressing superfluous features, improving network sensitivity and accuracy to feature information, and improving information flow; introducing the Transformer module to capture longer speech context feature association and combine local features to improve the accuracy and robustness of the model in sound event detection and localization tasks. The proposed FE-SELDnet significantly outperforms the original baseline network, according to experimental results on the TUT Sound Events dataset. The error rate decreased from 0.45 to 0.326, the SED and DOA scores decreased from 0.45 and 0.32 to 0.26 and 0.25, respectively, and the F₁ score increased to 79.4%. The algorithm proposed in this paper has higher superiority.