## 4 Conclusions  
The novelty of this work lies in studying the dependence of classification success on the number of species for bird sound classification. Furthermore, the idea is to illustrate how these classification results are heavily contingent on the composition of bird species subsets. Therefore, we employed balanced subsets of bird sounds for \(n\) species, drawing the species randomly from a larger dataset containing 659 species, where \(n\) was varied between 10 and 300. For each \(n\), we repeated the whole procedure (composition of the subset, training of the classifier, and testing) 20 times to produce a reliable estimate of the performance given a certain number of bird species. The classification was performed using a shallow feed forward neural network trained on 45 pre-computed sound features. We used a shallow neural network to conduct our analysis primarily due to its model simplicity, lower computational costs, and relatively low amount of data that is required to train such networks vis a vis deep neural networks. We wanted to benchmark the classification performance and perform our analysis using a simple model that can be trained using hand crafted sound features. We evaluated the classification performance using several common measures for classification success and also analyzed their dependence on \(n\) in detail. We observed that the classification performance was relatively high, even when many different species were present in the datasets under study and using relatively less data. This is an interesting result, since many recent approaches are based on deep neural networks trained on much larger datasets of images of spectrograms without any feature selection. This suggests that shallow neural networks trained on pre-computed sound features can also provide a robust approach to bird classification, which, at the same time, is inexpensive in terms of the computational costs and the amount of data used. Figure 8: The confusion matrix for a classification run with \(n=20\) species displays that the miss-classifications are spread among several classes. This can result in an overestimation of the averaged AUC for multi-class classifications. Concerning the robustness of the approach, we found that all measures of classification success showed a decline in value if the number of species present in the subset was increased. For some of these measures, this decline can be explained analytically knowing the n-dependence of the confusion matrix and assuming the behavior of an idealized perfect classifier. Additionally, we observe that the classification success depends on the individual composition of the bird subsets and classification results can vary significantly depending on the choice of species chosen for the analysis. For this reason, it seems the generic claims about the performance of a certain algorithm for, say \(n\) species of non-randomly drawn species, must not be interpreted as a generalized measure of performance for any \(n\) species. The classification results might not generalize for another set of \(n\) species, even when the species are drawn from the same dataset. Conceptualization, methodology, coding, validation, writing, B.G. ; Conceptualization, methodology, review, S.H. All authors have read and agreed to the published version of the manuscript. Received financial support from the project titled _AuTag BeoFisch_ (LFF-FV91) funded by the Landesforschungsforderung Hamburg. Not applicable. A publicly available dataset was analyzed in this study. The dataset has been drawn from the Xeno-Canto repository for bird sounds and was composed by the organizers of BirdClef2019 [15,16]. We are grateful to the creators of the Xeno-Canto repository for providing the excellent dataset of bird recordings, which was the basis for this study. We thank [PERSON] and [PERSON] for fruitful discussions and Landesforschungsforderung Hamburg for their financial support. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: