# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 3 Experiments
### Architecture and Learning Process  
Deep learning, particularly convolutional neural networks (CNNs), is well suited for analyzing the time-frequency representations of audio signals, such as spectrograms. In this study, we selected a deep residual CNN due to its ability to effectively learn spatial patterns in 2D acoustic feature maps while preserving gradient flow through skip connections. Compared to traditional machine learning models, CNNs offer superior performance on image-like input data and can automatically extract hierarchical features without manual feature engineering. Residual architectures, in particular, are known for their robustness in training deep networks and preventing vanishing gradients. While this approach requires more computational resources and a larger amount of training data, it provides strong generalization capabilities when sufficient data are available.  
Figure 14: Linear-Frequency Cepstral Coefficients (LFCCs).  
Figure 13: Mel-Frequency Cepstral Coefficients (MFCCs).  
Figure 15: Bark Spectrogram.  
The proposed architecture is a deep residual convolutional neural network designed to process and analyze eight input images of (256 \(\times\) 256) size. It consists of multiple convolutional layers with residual connections, which help efficiently extract hierarchical features while maintaining gradient stability. The network follows a ResNet-inspired design, with skip connections facilitating better feature propagation and mitigating vanishing gradients. Group normalization is applied throughout the network to stabilize activations and accelerate convergence. The model gradually reduces spatial dimensions while increasing the number of feature channels, enabling it to capture local and global patterns effectively. Following feature extraction, a global pooling layer condenses the information, which is then processed by fully connected layers. The final output consists of six continuous values representing different predicted parameters. The use of GELU activation and dropout regularization ensures stability and prevents overfitting. The architecture is shown in Figure 16.  
Table 3 below outlines the specifications of each layer, including output dimensions, filter size, and stride.  
The training process focuses on optimizing a deep learning model using the Adam optimizer, set with a learning rate of 0.0002 and betas (0.5, 0.9) to ensure stable and efficient convergence. Training is conducted for 50 epochs per configuration, utilizing a batch size of 32, balancing computational efficiency and performance.  
Each training iteration follows a structured pipeline: forward propagation, where the model processes input data and generates predictions; loss computation, using the Mean Squared Error to evaluate deviations across six continuous outputs; and backpropagation, where the model updates its weight based on gradients derived from the loss.  
To further refine training dynamics, a learning rate scheduler adaptively adjusts the learning rate based on model performance, reducing it when progress slows. This prevents stagnation in optimization and allows for finer weight updates as training progresses.  
Figure 16: The architecture of the deep residual convolutional neural network used in the experiments.