## 1 Introduction  
Early detection of emergencies and security monitoring in public places are fundamental issues in contemporary society. Faced with the growing need to safeguard lives and property, technological solutions have evolved to offer practical and efficient responses to these challenges. In this context, deep learning emerges as a powerful and promising tool for transforming how we face these critical scenarios [1]. Applying deep learning to detecting events and situations presents an innovative perspective and opens a range of unsuspected possibilities regarding security and emergency response. Imagine a world where surveillance systems watch, record, understand, and act. This is precisely what deep learning has the potential to achieve. At its core, deep learning consists of building and training models of artificial neural networks inspired by how the human brain works [2]. These models can learn complex patterns and features from massive data sets, making them ideal for analyzing audio signals, such as the characteristic sounds of specific events and situations. Deep learning can discern these sounds and trigger responses in real time, from a crying baby to breaking glass or emergency sirens [3]. One of the most impressive applications of this technology is security monitoring in public places. In environments such as stadiums, airports, train stations, and other crowded spaces, intelligent audio systems can detect disturbances in sound patterns and instantly recognize abnormal situations such as fights, cries for help, or even gunshots. These systems alert the corresponding authorities and activate preventive measures, such as access blocking or evacuation guidance. In addition to real-time monitoring, early detection of emergencies becomes an invaluable resource for public safety [4]. A deep learning system can continuously process and analyze audio data, identifying patterns that suggest risky situations, such as fires, structural collapses, or accidents. By anticipating these circumstances, authorities can respond more quickly and effectively, minimizing damage and saving lives. The advantages of this proposal are diverse and transcend the limits of security. Implementing event and situation detection systems based on deep learning can drastically reduce emergency response time, which is crucial for medical attention and the intervention of firefighters and rescue teams [5]. Likewise, by allowing precise and automated surveillance, human resources are optimized and freed up for more complex tasks. The detection of events and situations through deep learning is a process that involves the use of convolutional neural network (CNN) algorithms [6]. These algorithms are inspired by the structure and functioning of the human visual system and stand out for their ability to analyze and extract relevant features from complex data, such as audio spectrograms. Spectrograms are visual representations of sound signals over time and frequency, allowing the algorithm to capture subtle details and patterns in acoustic signals. The execution method of this algorithm involves a series of interconnected stages that culminate in the detection and classification of sound events. First, it is based on vast audio data representing specific situations and events, such as emergency sirens or screams, music, etc. These data are used to train the CNN, exposing it to various variations of the target sounds. During this training process, the neural network automatically adjusts its internal weights and parameters to learn to identify distinctive features and patterns associated with each event [7] Once the neural network has been trained, it is validated and fine-tuned. This involves using separate data sets that have not been used during training, which allows for evaluation of the generalization capacity of the algorithm. The system is then ready for deployment in the monitoring or surveillance environment. In real time, the algorithm converts audio signals into spectrograms, which the neural network processes [8].