## 1 Introduction
In recent years, deep learning has rapidly developed across various fields [8]. Based on artificial neural networks, deep learning employs a multi-layered network structure for feature learning and pattern recognition. The key lies in training the network model to extract data features, enabling efficient classification, regression, and generation tasks. Researchers both domestically and internationally have made progress in applying deep learning to the study of wind turbine and airfoil noise. [PERSON] et al. [9] proposed a novel ice thickness detection method based on changes in the far-field noise of iced airfoils, establishing a multivariable input neural network prediction model that can predict the maximum ice thickness based on flight parameters and total sound pressure level increments, effectively improving the accuracy and reliability of ice detection. [PERSON] et al. [10] introduced a method utilizing convolutional neural networks (CNN) and a specialized airfoil noise database to predict the noise of wind turbine airfoils. This model effectively predicts the noise generated by wind turbine airfoils and serves as an effective tool for assessing airfoil noise and designing low-noise blades, achieving a high prediction accuracy with a Mean Absolute Percentage Error (MAPE) of less than 0.0531%. [PERSON] et al. [11] constructed a new dataset named CAI-SWTB using deep learning algorithms, employing five basic CNN architectures for wind turbine blade fault detection and exploring transfer learning techniques. The transfer learning Xception model achieved an accuracy of 99.92% on the CAI-SWTB dataset. [PERSON] et al. [12] developeda prediction model based on deep neural networks (DNN) to more accurately predict airfoil self-noise, especially turbulent boundary layer trailing edge noise, using NASA's experimental noise dataset for training. Their research concluded that the DNN model is an effective and accurate tool for predicting TBL-TE noise, and further studies revealed that the DNN model can uncover the physical mechanisms behind TBL-TE noise, providing more guidance for airfoil design and noise reduction technologies. The aforementioned studies demonstrate the applicability of deep learning techniques, particularly CNN, in airfoil/blade identification and noise characterization. The image recognition capabilities of CNN align well with the structural feature extraction requirements in this study, enabling effective pattern recognition of airfoil noise sources and flow-acoustic coupling mechanisms. This methodology provides a robust framework for data-driven noise prediction and optimization in our research. Currently, researchers both domestically and internationally commonly use wind tunnel tests, BPM semi-empirical theories, and CFD methods to calculate the aerodynamic noise of wind turbine airfoils. However, there is relatively little research on the application of deep learning techniques for predicting airfoil noise. This field faces challenges such as a limited existing database of wind turbine airfoils [9], large computational demands for noise prediction, and low efficiency. To solve these problems, this paper presents an airfoil noise prediction model based on a generalized airfoil set and deep learning. A generalized airfoil dataset with rich curvature, thickness, and trailing edge features can be generated based on common 31 kinds of airfoil shapes. The airfoil integrated theory; B-spline function and CST method are introduced to express the common airfoils, which makes the airfoil dataset more comprehensive. This approach aims to enhance the generalization ability of deep learning models in the field of wind turbine noise prediction. The generated dataset is then used to train the ResNet18 model, which is compared with other common machine learning models to validate its performance and investigate its computational efficiency.