Few-shot Long-Tailed Bird Audio Recognition
This incremental work assists researchers in monitoring bird populations and biodiversity through automated audio analysis.
The paper tackled bird audio recognition from complex soundscapes using weak labels and limited data, achieving 18th place out of 807 teams in the BirdCLEF 2022 Challenge.
It is easier to hear birds than see them. However, they still play an essential role in nature and are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Deep Neural Networks allow us to process audio data to detect and classify birds. This technology can assist researchers in monitoring bird populations and biodiversity. We propose a sound detection and classification pipeline to analyze complex soundscape recordings and identify birdcalls in the background. Our method learns from weak labels and few data and acoustically recognizes the bird species. Our solution achieved 18th place of 807 teams at the BirdCLEF 2022 Challenge hosted on Kaggle.