It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to detect and classify bird sounds, by doing this, we can assist researchers in monitoring the status and trends of bird populations and biodiversity in ecosystems. We propose a sound detection and classification pipeline for analyzing complex soundscape recordings and identify birdcalls in the background. Our pipeline learns from weak labels, classifies fine-grained bird vocalizations in the wild, and is robust against background sounds (e.g., airplanes, rain, etc). Our solution achieved 10th place of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle.

中文翻译：

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野外鸟类声音的弱监督分类和检测。BirdCLEF 2021 解决方案

听鸟鸣比看鸟容易，然而，它们仍然在自然界中发挥着重要作用，它们是环境质量和污染恶化的极好指标。机器学习和卷积神经网络的最新进展使我们能够检测和分类鸟类的声音，通过这样做，我们可以帮助研究人员监测生态系统中鸟类种群和生物多样性的状态和趋势。我们提出了一种声音检测和分类管道，用于分析复杂的音景记录并识别背景中的鸟叫声。我们的管道从弱标签中学习，对野外细粒度的鸟类发声进行分类，并且对背景声音（例如飞机、雨等）具有鲁棒性。在 Kaggle 举办的 BirdCLEF 2021 挑战赛中，我们的解决方案在 816 支队伍中获得了第 10 名。