Environmental Noise Recognition Method Based on Multi-Channel Resolution Features and Attention Mechanism
Xincheng Mu, Lizhi Liu · 2025
In order to solve the problem of insufficient accuracy of the existing environmental noise classification models, an environmental noise classification network based on multi-channel discrimination and attention mechanism was proposed. First, multi-channel resolution features are used to more comprehensively represent and capture the diversity and complexity of audio features. Then, a second-order dense connection method was used to replace the dense connection in DenseNet to solve the overfitting problem caused by the high similarity and redundancy of the sound signal. Finally, a G-SE Layer module is used for acoustic signals, which enhances the model's attention to important features by efficiently fusing channel features. Experimental results show that compared with the original DenseNet network, the improved GS-2DenseNet has an improved recognition rate of environmental noise by 4.83%, and the accuracy and F1-score are higher than those of acoustic networks such as ResNet and EcapaTdnn.