## 3 Results
### Acoustic Modeling
#### 3.3.3 Performance Evaluation
There were a few errors, though, such as 12 \"Whistle\" events incorrectly classified as \"Screams\", 5 as \"Voices\", 10 as \"Traffic\", 5 as \"Bell\", and 10 as \"No Event\". Finally, the algorithm could also correctly identify the absence of audible events in the \"No Event\" category, with 960 cases adequately classified as such. However, it made errors, including 35 misclassified as \"Screams\", 30 as \"Voices\", 10 as \"Traffic\", 12 as \"Bell\", and 15 as \"Whistle\". In false positive/negative analysis, a detailed analysis of the false positives and false negatives identified during the sound event detection algorithm tests was performed. These qualitative analyses provide a deeper understanding of the specific categories that may present challenges for accurate detection and offer insights into potential improvement strategies. False positives:  
* Category: Shouts  
False Positives: 20 in Data Set 1, 15 in Data Set 2. Description: the algorithm tends to classify some \"Voices\" and \"Timbre\" events as \"Screams\". This can be attributed to the similarity in the acoustic characteristics between these sounds, such as high pitches and rapid changes in amplitude. Improvement Strategies: Including more specific spectral characteristics could be considered to distinguish between \"Screams\" and other similar categories. Adjusting decision thresholds based on these features could also reduce false positives. * Category: Traffic  
False Positives: 5 in Data Set 1, 8 in Data Set 2. Description: the algorithm sometimes confuses \"Whistle\" and \"Bell\" events with \"Traffic\". This could be due to high-frequency components that resemble traffic noise in these sounds. Improvement Strategies: Incorporating contextual information, such as sound duration and energy, could help distinguish between \"Traffic\" and other categories better. Also, adjusting the parameters for detecting frequency peaks could reduce these false positives. False Negatives:  
* Category: Voices  
False Negatives: 10 in Data Set 1, 5 in Data Set 2. Description: sometimes \"Voices\" events are misclassified as \"Screams\" or \"Bell\", resulting in false negatives for the \"Voices\" category. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Actual Category\(\backslash\)** & **Screams** & **Voices** & **Traffic** & **Timbre** & **Whistling** & **No Event** \\
**Predicted Category** & & & & & & \\ \hline Screams & 920 & 15 & 10 & 8 & 5 & 20 \\ Voices & 5 & 930 & 20 & 10 & 15 & 25 \\ Traffic & 8 & 12 & 900 & 5 & 20 & 15 \\ Timbre & 5 & 8 & 5 & 950 & 8 & 10 \\ Whistling & 12 & 5 & 10 & 5 & 920 & 10 \\ No Event & 35 & 30 & 10 & 12 & 15 & 960 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of sound event detection by category (Data Set 2). Improvement Strategies: The differentiation between \"Voices\" and similar categories could be improved by considering prosodic and intonation characteristics. Tuning voice segmentation algorithms could also help reduce these false negatives. * Category: Whistle
* False Negatives: 12 in Data Set 1, 10 in Data Set 2. * Description: the algorithm sometimes has difficulty distinguishing between \"Whistle\" and other high-frequency categories such as \"Bell\" and \"Screams\". * Improvement Strategies: including features that capture the characteristic modulations of the \"Whistle\" and the adjustment of the event detection parameters could improve the accuracy in detecting this category. In the learning curve of Data Set 1, the following is observed:
* Accuracy: as the size of the training set increases, the algorithm's accuracy tends to increase gradually and then level off around 92%. * Completeness: completeness increases as larger training sets are used, reaching around 88%. * F1-score: the F1-score experiences a significant increase as the size of the training set increases and then stabilizes around 90%.