Ghani, B.; Hallerberg, S. A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification. Appl. Sci.2021, 11, 9226.
Ghani, B.; Hallerberg, S. A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification. Appl. Sci. 2021, 11, 9226.
Ghani, B.; Hallerberg, S. A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification. Appl. Sci.2021, 11, 9226.
Ghani, B.; Hallerberg, S. A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification. Appl. Sci. 2021, 11, 9226.
Abstract
The automatic classification of bird sounds is an ongoing research topic and several results have been reported for the classification of selected bird species. In this contribution we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate in detail if and how classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300, randomly drawing sounds of species from a dataset of 659 bird species taken from Xeno-Canto database. We observe that the shallow artificial neural network trained on pre-computed sound features is able to classify the bird sounds relatively well. The classification performance is evaluated using several common measures such as precision, recall, accuracy, mean average precision and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depends on the individual composition of the subset and varies across 20 randomly drawn subsets.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
