### Related Works
This approach not only strengthens the core methods discussed in our manuscript but also addresses overfitting concerns, thereby enhancing the overall recognition effectiveness indeed. More importantly, the application of transfer learning has the potential to improve the performance of ESC models. By pre-training on ESC tasks, the core recognition model can obtain better initial parameters and feature representations. It helps algorithms converge quickly and achieve optimal solutions within the hyperplane, resulting in better performance on the validation set. Therefore, transfer learning is becoming increasingly important in the study of environmental sound classification [15]. Some conventional computer vision methods are being expanded. Network architectures tailored for image analysis have gradually ventured into ESC applications [14; 16]. However, current ESC task methods using pre-trained Convolutional Neural Networks (CNNs) often resort to redefining the last layer to address sound classification challenges [11]. A notable limitation of this approach lies in its inability to capture the intricate long-term dependency relationships in audio signals [17], and since most of these models are transferred from the image domain, they focus more on the physical meaning and distribution characteristics of images, requiring higher training weights for parts that can effectively reflect abstract feature distribution characteristics and semantic cues. Thus, merely fine-tuning the last output layer and directly applying it to ESC tasks may not yield good results. Consequently, attention mechanisms, as representative optimization strategies, are progressively garnering more and more attentions all over the world. These mechanisms offer a promising avenue to address the limitations of traditional CNN-based approaches, enhancing the model's capability to exploit critical temporal and contextual information within audio signals in the real-world situation. As attention mechanisms are introduced into the field of sound classification, some research has improved pre-trained network models by seamlessly integrating attention mechanisms [7]. In [3], a ResNet model based on a multi-feature attention mechanism is proposed, which added a Net50_SE attention module to suppress the impact of environmental noise. By redistributing the training weights of the sound signals, this improvement has achieved higher recognition accuracy in noisy environments. Experimental evidence underscores the adaptability of certain attention modules, which can cater to the fine-tuning demands of transfer learning by dynamically adjusting parameter weights. This adaptability empowers the model to interpret increasingly intricate audio signals. Consequently, many previous studies are exploring attention-based solutions [18], with models based on Transformer concepts demonstrating strong feature learning capabilities [19]. The Audio Spectrogram Transformer (AST) model is proposed in [20], and experiments show that AST pre-trained on ImageNet and AudioSet datasets have achieved advanced results in various downstream audio tasks. Using pre-trained models can achieve better generalization on smaller datasets, helping the model obtain better initial parameters and improve training efficiency. However, due to factors such as noise and dataset size, pre-trained models in ESC tasks are also prone to overfitting. Researchers have endeavored to address overfitting through various strategies, including parameter reduction [21] and data augmentation [12]. These methods can only partially reduce overfitting and cannot completely eliminate this issue, which significantly constrains the development of urban environmental sound recognition algorithms, especially in high-noise environments where this disadvantage is amplified. Therefore, solving the overfitting of pre-trained models in downstream tasks is also crucial. At present, the research on urban environment sound recognition tasks in flexible and noisy environments remains untouched.