论文标题
几乎没有长尾鸟音频识别
Few-shot Long-Tailed Bird Audio Recognition
论文作者
论文摘要
听到鸟比看到它们要容易得多。但是，它们仍然在自然界中起着至关重要的作用，并且是环境质量和污染恶化的极好指标。深度神经网络的最新进展使我们能够处理音频数据以检测和对鸟类进行分类。该技术可以帮助研究人员监测鸟类种群和生物多样性。我们提出了一个声音检测和分类管道，以分析复杂的音景记录并在背景中识别鸟类。我们的方法从弱标签和很少的数据中学习，声学上可以识别鸟类。我们的解决方案在Kaggle举办的Birdclef 2022挑战赛中获得了807支球队的第18位。
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.
