Effects of landscape and distance in automatic audio based bird species identification

The present work focuses on how the landscape and distance between a bird and an audio recording unit affect automatic species identification. Moreover, it is shown that automatic species identification can be improved by taking into account the effects of landscape and distance. The proposed method uses measurements of impulse responses between the sound source and the recorder. These impulse responses, characterizing the effect of a landscape, can be measured in the real environment, after which they can be convolved with any number of recorded bird sounds to modify an existing set of bird sound recordings. The method is demonstrated using autonomous recording units on an open field and in two different types of forests, varying the distance between the sound source and the recorder. Species identification accuracy improves significantly when the landscape and distance effect is taken into account when building the classification model. The method is demonstrated using bird sounds, but the approach is applicable to other animal and non-animal vocalizations as well.

Automated summary

The study explores the impact of landscape and distance on automatic species identification in birds and demonstrates that considering these factors can enhance the accuracy of identification. A method is proposed that uses impulse responses to modify existing bird sound recordings, which are measured in real environments. The method is demonstrated in different landscapes and distances, showing significant improvements in classification accuracy. The approach is not limited to bird sounds and can be applied to other animal and non-animal vocalizations.