## 3 Results and Discussion
### Comparing Different Measures for Classification Success  
In this contribution, we used several common measures for evaluating the classification performance and comparing their results. The primary reason for this is that different indices encapsulate different aspects of the classification performance. Secondly, as mentioned earlier, there seems to be no consensus in the literature available on the choice of evaluation metric for the audio-based bird species classification task. This compelled us to study a set of indices and not rely on a specific metric. As one can see in Figure 3, the precision and recall for \(n<100\) do not show much disparity and instead look quite similar. Although, by definition, these two indices encapsulate different aspects of model performance. This can be clearly seen in Figure 7. Here, we see that for different species in one classification run of \(n=10\), the precision and recall values differ. There are species where the precision is higher than the recall (e.g., species 10) while others where the recall is higher than the precision (e.g., species 3). It seems that, for \(n<100\), the precision and recall values more or less equalize when an average is taken over a species. From Figures 3 and 4, one can additionally see that the accuracy is exactly the same as the recall, since the equations of recall and accuracy become the same when averages are taken over all classes. Additionally, the mean average precision (mAP) was used to evaluate classification success. Increasingly a number of works in recent years have used this metric to state the classification performance of their models. Note that average precision is one way of measuring the area under the precision-recall curve. Compared to precision and recall that are computed for one probability threshold, the average precision is computed cumulatively by varying the threshold. We see in Figure 3 that, although it follows a similar downward quadratic trend to the recall and precision, the mAP values are slightly higher than the precision and recall values for different \(n\). For instance, the range for \(n=10\) species for the 20 runs is between 0.86 and 0.94, whereas the precision and recall ranges are between 0.79 and 0.87. This observation also reflected in the offset of the functions describing the \(n\)-dependence as mentioned above. Figure 6: The precision (cTPs/cPs) increases as the confidence threshold (probability a sample needs to be assigned by the model in order for it to be classified as a positive prediction) is varied between 0.5 and 1.0. Shown are precision values for \(n=30,60,90\), and 300. As we can see for different \(n\), as the confidence threshold tends toward 1.0, the precision also increases significantly. Another commonly used metric for classification success is the area under the Receiver Operating Characteristics curve (AUC). Our model achieved a high score on the AUC metric as can be seen in Figures 3 and 4. Although the AUC score decreased with the increase in number of species \(n\), the score was nevertheless unexpectedly high. For instance, the AUC score for \(n=300\) for one run was 0.94, which is unexpected for such a large number of species. (Note that, as per the definition of the AUC, a random classifier making randomized decisions should give a score of 0.5). In our understanding, the multi-class nature of our problem explains this result. As mentioned earlier, the AUC metric was essentially designed for a binary classifier and was later generalized for multi-class classification problems [36]. Therefore, in the case of multi-class problems one needs to binarize the class labels to compute the AUC score, such that the problem is transformed into a binary classification problem with \(\frac{n(n-1)}{2}\) binary classifiers (where \(n\) is number of classes).