Automated Acoustic Classification of Bird Species from Real -Field Recordings

We report on a recent progress with the development of an automated bioacoustic bird recognizer, which is part of a long-term project, aiming at the establishment of an automated biodiversity monitoring system at the Hymettus Mountain near Athens. In particular, employing a classical audio processing strategy, which has been proved quite successful in various audio recognition applications, we evaluate the appropriateness of six classifiers on the bird species recognition task. In the experimental evaluation of the acoustic bird recognizer, we made use of real-field audio recordings for seven bird species, which are common for the Hymettus Mountain. Encouraging recognition accuracy was obtained on the real-field data, and further experiments with additive noise demonstrated significant noise robustness in low SNR conditions.