## 3 Results
### Data Collection  
The collection and generation of data play a fundamental role in developing and evaluating the sound event detection algorithm in a university environment. This crucial stage allows the construction of a diverse and realistic data set that reflects an academic campus's complexities and acoustic variability. Recording sessions have been conducted in different locations to capture the authentic essence of the sound events on a university campus. This includes classrooms, hallways, libraries, common areas, and outdoor spaces. Various good events were recorded during these recordings, such as conversations, footsteps, doorbells, background noise, and other sounds typical of an academic environment. In addition to recordings of actual sound events, background noise recordings have been collected at different times of the day and various locations on campus. These recordings were used to capture typical environmental sounds, such as the murmur of distant conversations, the hum of devices, traffic noise, and other sounds that contribute to the acoustic environment on a college campus. Using recordings of actual sound events and background noise data, data is generated that represents a variety of situations and contexts. Using signal processing, layering,and mixing techniques, stages have been created that mimic the acoustic complexity of a college campus. A crucial step in data generation has been acoustic modeling and feature fitting. For this, the unique acoustic properties of each campus area, such as reverberation, attenuation, and sound direction, have been considered. Through modeling techniques, the acoustic characteristics of good events have been adjusted to align with the properties of the corresponding environment. With the rich and diverse data set, a series of test scenarios representing a variety of university contexts were designed. Each system comprises a specific combination of sound events and background noise levels. These scenarios allow for evaluation of the ability of the algorithm to detect and classify good events in varied and challenging situations. Data collection and generation play a critical role in evaluating our algorithm. Table 3 details the specificities of the recordings for each situation or event analyzed. An in-depth process was carried out to ensure a diverse and representative sample regarding the number of recordings and duration. The signal-to-noise ratio indicates the quality of the recording in terms of the distinction between the sound of interest and background noise. With a wide range of recordings varying in length and quality, the data set provides a solid foundation for training and validating the proposed algorithm. The table specifies the data considered in the sound metadata. In the \"Ambient Sounds\" events, children's crying is included. The composition is as follows:  
Environmental sounds--250 recordings represent general ecological sounds that could be present in situations where infant crying could also occur, such as background chatter, traffic, and soft music, among others. These environments simulate a realistic and challenging context for the algorithm, ensuring that it can discern crying even with environmental distractions. Specific infant crying--250 recordings capturing crying episodes of different babies in various contexts. These ambient sounds, specially designed for the context of infant crying, should not be confused with background sounds that could be used for other events, such as emergency sirens or breaking glass. Although some environmental sounds could be reused in different contexts, each data set was prepared considering the specificities and challenges of each sound situation. For the training and validation of our model, the data set was divided into training (70%), validation (15%), and testing (15%). This division, based on standard practices in machine learning, ensures:  
* Training: a sufficient basis for the network to recognize distinctive features and patterns of acoustic events.