### Related Works  
Unlike musical and speech audio signals, urban environmental sounds consist of indoor and outdoor conditions, which contain random and unpredictable background noise. This results in more complex characteristics of environmental sound information, making it a primary issue in the ESC field to extract such features and reduce the impact of noise on model recognition accuracy. In the existing research on audio signals, traditional feature extraction methods often focus on time domain information, spatial information, frequency domain characteristics, etc. In recent years, feature extraction approaches combining time frequency characteristics with statistical characteristics have played a significant role, such as Mel Spectrogram, Log-Mel Spectrogram, Mel-Frequency Cepstral Coefficients (MFCC), and Gammatone Frequency Cepstral Coefficients (GFCC) [6]. Due to different extraction methods, these acoustic features have unique application scenarios and advantages [7]. With the development of deep learning, many researchers have transcended the limitations of relying solely on individual acoustic features [8], instead adopting feature fusion to integrate multiple acoustic features to harness their collective strengths [9]. For example, [10] analyzed various acoustic features and experimentally studied whether different feature fusions affect classification results. Experiments demonstrate that some feature fusions, such as MFCC and Chroma STFT, can enhance classification model accuracy. In [11], logarithmic operations on Log-Mel spectrograms produced Log2 Mel (L2M) and Log3 Mel (L3M). The fusion of Log-Mel with L2M and L3M results in new representations, making it easier for these feature sets to capture detailed texture characteristics and abstract features in ESC tasks and, thus, achieves advanced results in recognition methods based on this. In [12], experiments show that duplicating the Log-Mel spectrogram into three channels as input features yielded higher classification accuracy compared to a single Log-Mel spectrogram. When single features cannot sufficiently represent signals, using multi-feature fusion to jointly represent signals is an effective solution. However, at the current research stage, whether using splicing techniques [11] or dimensional stacking [12] to fuse features, it undoubtedly significantly increases the dimensionality of feature vectors. This approach underscores the potential of exploiting feature diversity and redundancy to refine model performance in complex urban environments. Therefore, it is necessary to seek new solutions on this basis. In order to represent acoustic signals, the proper and clever selection of classification models in ESC tasks goes hand in hand with representing acoustic signals effectively. In [13], a sub-spectrogram segmentation with score level fusion framework for ESC is proposed, along with a Convolutional Recurrent Neural Network (CRNN) to improve classification accuracy. To tackle challenges such as vanishing gradients, high network loss, and low accuracy, [14] propose Long Short-Term Memory Neural Networks (LSTM) and Gated Recurrent Unit (GRU) neural networks, constructing a deep recurrent neural network model. However, recognizing environmental sounds in noisy scenarios typically poses a unique challenge due to the sheer diversity and often imbalanced nature of sound data. Some categories of data may be relatively abundant, while others are very scarce. Through transfer learning, pre-trained models on data-rich categories can be transferred to data-scarce categories and, thus, improves overall data utilization. This can significantly reduce the computational resources needed to train models from scratch. Furthermore, the direct training of core models on limited datasets in ESC tasks can easily lead to overfitting. Transfer learning can enhance the recognition effect of the core methods in this paper by leveraging models pre-trained on extensive datasets.