Bird species identification using spectrogram and dissimilarity approach

In this work, we investigate bird species identification starting from audio recordings on eight quite challenging subsets taken from the LifeClef 2015 bird task contest database, in which the number of classes ranges from 23 to 915. The classification was addressed using textural features taken from spectrogram im-ages and the dissimilarity framework. The rationale behind it is that by using dissimilarity the classification system is less sensitive to the increase in the number of classes. A comprehensive set of experiments confirms this hypothesis. Although they cannot be directly compared to other results already published because in this application domain the works, in general, are not developed ex-actly on the same dataset, they overcome the state-of-the-art when we consider the number of classes involved in similar works. In the hardest scenario, we obtained an identification rate of 71% considering 915 species. We hope the subsets proposed in this work will also make future bench-marking possible.