## 3 Results
### Acoustic Modeling
#### 3.3.3 Performance Evaluation
* In the learning curve of Data Set 2, the following is observed:
* Accuracy: like the first data set, the accuracy shows a steady improvement as the size of the training set increases, reaching around 91%. * Completeness: completeness gradually increases as larger training sets are used, reaching approximately 85%. * F1-score: the F1-score in Data Set 2 follows a similar trend to Data Set 1, rising and stabilizing around 88%. Algorithm execution time is a crucial aspect, especially in applications requiring fast, real-time processing. Therefore, the average time the algorithm takes to process an audio signal and generate results on different data sets was evaluated. In addition, these times are compared with the requirements in real time. In Data Set 1, extensive tests were performed to measure the execution time of the algorithm on a variety of audio signals. The average processing time per signal was approximately 12 milliseconds. This time includes feature extraction, classification, and output generation. In Data Set 2, similar tests were carried out to assess execution time. In this case, the average processing time per audio signal was around ten milliseconds. It is important to note that this data set was more complex regarding the sound events present. By analyzing the runtime results, the algorithm meets real-time requirements in many environments. For example, in real-time monitoring applications where timely detection of sound events is essential, such as in security systems or anomaly detection, the recorded processing times are low enough to allow effective implementation. The algorithm's applicability in different environments depends on application-specific runtime constraints. Although the algorithm shows satisfactory performance in terms of time in most cases, it is essential to consider that specific extremely time-sensitive environments, such as high-speed real-time response systems, may require additional optimizations. The learning curve graph presented in Figure 3 offers insight into the relationship between the size of the training set and the accuracy of the sound event detection algorithm. As the size of the training set increases, both the training accuracy and the validation show an increasing trend. Initially, with a smaller training set (around 100 samples), the training accuracy is relatively low, suggesting some difficulty for the model in capturing the complexities of sound events. However, as the training set expands, the training and validation accuracy gradually increases, indicating an improvement in the algorithm's generalizability. It is interesting to note that the gap between the accuracy curves in training and validation tends to decrease as the size of the training set increases. This suggests the model achieves a more optimal balance between fit to the training data and generalizability to new data. However, after a certain point, around 800 training samples, increasing the size of the training set has a more marginal impact on improving accuracy. This could indicate that the model has already captured most of the relevant features of the sound events present in the data and that a further increase in the amount of data does not result in significant improvements in accuracy. In summary, the learning curve plot provides valuable insight into how the size of the training set influences the performance of the sound event detection algorithm, guiding informed decisions on the balance between computational cost and accuracy improvement. Several specialized techniques were applied during the training and validation phases to improve the accuracy of the algorithm and address the specific challenges of the data set:  
* Reweighting of Classes: given the imbalance observed in the number of sound events for specific classes, a reweighting was introduced. For example, emergency alarms, which represented only 2% of the total data set, were reweighted with a factor of 5. In contrast, representing 50% of the data set, general conversations were reweighted with a factor of 0.8.