Improving Bird Identification using Multiresolution Template Matching and Feature Selection during Training

This working note describes methods to automatically identify a large number of different bird species by their songs and calls. It focuses primarily on new techniques introduced for this year’s task like advanced spectrogram segmentation and decision tree based feature selection during training. Considering the identification of dominant species, previous results of the LifeCLEF Bird Identification Task could be further improved by 29%, achieving a mean Average Precision of 59% (mAP). The proposed approach ranked second place among all participating teams and provided the best system to identify birds in soundscape recordings.