## 2 Materials and Methods
### Physical Infrastructure for the Implementation of Sound Detection  
The successful implementation of the deep learning-based sound event detection system requires a robust and optimized physical infrastructure that allows for efficient data processing and the execution of complex algorithms. Figure 2: Flowchart of the process of detecting and classifying sound events through deep learning. The performance of the sound event detection system depends mainly on the hardware used. For the development of this work, equipment with basic specifications has been considered, such as a high-speed multi-core processor, which is essential to carry out complex calculations efficiently during the training and inference of the deep learning model. An adequate amount of RAM ensures the system can handle extensive data and processing operations simultaneously. A GPU that supports parallel computation will significantly speed up model training, resulting in a faster and more efficient system. The high-capacity hard drive is essential for storing audio data sets and deep learning models. The implementation of the system requires the use of specialized software and libraries. It is essential for development to use tools such as an integrated development environment (IDE) that supports programming in Python and facilitates project management. Python programming is necessary for implementing deep learning algorithms and manipulating data. Libraries such as TensorFlow2.13.0, Keras2.14.0, or PyTorch2.0.1 are used to develop and train sound event detection models. Libraries like Librosa0.10 or SciPy1.9.2 are helpful in processing and analyzing audio signals [32; 33]. In terms of the connectivity, for the practical deployment of the sound event detection system, it is essential to consider the connectivity requirements, such as the connection to the network. The system requires internet access for data downloads, model updates, and detection results. Audio input devices, such as microphones or sensors, must be appropriately connected to the system to capture audio signals from the environment. The suitable hardware, specialized software, and convenient connectivity provide a solid foundation for developing and implementing the deep learning-based sound event detection system. The quality of the input data is essential to the performance and accuracy of the system. Therefore, development requires high-quality microphones and, in some cases, specialized sensors to accurately capture relevant sound signals in the environment. The selection of these components should be based on the specific characteristics of the signs to be detected. For example, Table 2 shows the specifications of the model XYZ123 microphone used in the recordings. This condenser microphone was selected for its high sensitivity and wide frequency range, allowing various ambient sounds to be accurately captured. The high signal-to-noise ratio ensures recording quality, reducing unwanted noise. This choice of equipment facilitates more accurate and reliable data collection for subsequent analysis. The proper selection and configuration of this physical infrastructure are essential to ensure optimal performance of the audible event detection system. Processing power, efficient storage, and low-latency network connectivity contribute significantly to the effective, real-time detection of emergencies and critical loud events. In addition to the basic technical requirements, it is essential to consider the scalability and long-term maintenance of the system. This may involve using cloud technologies and optimizing resources to handle increased data volumes and computational demands. In the application, if the system is in an outdoor environment, such as an emergency early warning system, it must be designed to withstand adverse weather conditions, such as rain, humidity, and extreme temperatures. Ultimately, the physical infrastructure and technical requirements provide the foundation for building and operating the audible event detection system.