## 2 Materials and Methods
### Concepts Used in the Development of the Method  
For developing the proposal, fundamental concepts that support detecting events or situations through deep learning are used. These concepts are essential to understanding how our approach aligns with advances in this area and how we leverage these fundamentals to achieve accurate and efficient detection.  
Deep learning is a branch of artificial intelligence based on interconnected artificial neural networks. Inspired by how the human brain works, these networks can learn and extract complex data patterns [23]. In our approach, we use deep learning architectures to analyze intricate features and abstract representations of audio signals, allowing the identification of specific events or situations.  
Audio Spectrograms: A spectrogram is a visual representation of the frequencies of a sound over time. Translating audio signals into spectrograms provides an efficient way to capture acoustic characteristics and temporal variations [24]. Our proposal uses audio  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Similar Work** & **Specific Approach** & **Generalization** & **Real-Time** & **Applicability Versatile** & **Efficiency** \\ \hline Crying Baby Detection [16] & Crying babies in homes & Limited & No & No & Low \\ Detection of Emergency Sirens [17] & Emergency sirens in & Limited & Yes & No & Moderate \\ Sirens [17] & urban areas & Limited & Yes & No & Moderate \\ Glass Break Detection [18] & Breaking glass in shops & Limited & Yes & No & Moderate \\ Explosions in urban environments & Limited & Yes & No & Moderate \\ Shot Detection [20] & Shooting in public & Limited & Yes & No & Moderate \\ Fire Detection [21] & fires in buildings & Limited & Yes & No & Moderate \\ Traffic Accident & Highway traffic & Limited & Yes & No & Moderate \\ Detection [22] & accidents & & & & Moderate \\ Detection of Events and & Various events and & & & & \\ Situations (Current Proposal) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative table of similar works in the detection of events and situations using deep learning.  
spectrograms as input for the neural networks, which allows our algorithm to analyze and understand the unique features of different sound events.  
CNN, Convolutional Neural Networks, are a class of neural networks designed explicitly for processing grid-shaped data, such as images and, in our case, spectrograms [25]. Convolutional layers can detect local features, such as edges and textures. We apply CNN in our model to capture relevant patterns in the audio spectrograms and perform event detection [26].  
Supervised Training: in supervised training, the deep learning model is tuned using labeled examples. For the approach of this work, we use annotated data sets containing audio recordings marked with information about the events or situations we want to detect. The model is trained to associate patterns in the spectrograms with the corresponding labels, allowing future event identification in new audio data.  
Multiclass Classification is used a problem where the goal is to assign a label to an entry from a set of mutually exclusive classes. In this case, we implement multiclass classification to label the audio spectrograms with the categories of events or situations that we want to detect [27]. Each class represents a specific sound event, and the model assigns the most likely label to each audio input.  
Combining these concepts provides a solid foundation for the event or situation detection proposal. By employing deep learning, audio spectrograms, convolutional neural networks, and supervised training, a robust and efficient approach can identify and classify specific sound events in real time, contributing to security monitoring and early detection of situations and emergencies in diverse, dynamic environments.