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.
翻译:听到鸟类比看到鸟类更容易。 但是,它们仍然在自然界中扮演着不可或缺的角色,是环境质量和污染恶化的极好指标。深海神经网络最近的进展使我们能够处理音频数据,以探测鸟类并对其进行分类。这种技术可以帮助研究人员监测鸟类种群和生物多样性。我们建议一个健全的探测和分类管道,以分析复杂的声景记录并识别背景中的鸟叫声。我们的方法从薄弱的标签和很少的数据中学习,声音也承认鸟类物种。我们的解决方案在卡格格尔举行的2022年鸟类CLEF挑战中达到了807个团队的第18位。