Research on Urban Sound Classification based on ConvNeXt-FECA Model

With the increasing severity of urban sound pollution, efficient and accurate urban sound classification and recognition has become an important topic in the field of urban environmental monitoring. In this paper, we propose an urban noise classification method based on the Frequency Enhanced Convolution Attention (ConvNeXt-FECA) model. By fusing the Spectrogram and MFCC features in the early stage, the method makes full use of the advantages of the two features, and introduces the Frequency Enhanced Convolutional Attention Mechanism (FECA) to adaptively pay attention to the changes in the frequency band of the audio signal, which effectively improves the classification performance. Experimental results show that the classification accuracy of the ConvNeXt-FECA model is 98.5% on the Urban Sound8K dataset, showing strong robustness and generalization ability.