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 Machine Learning and Convolutional Neural Networks allow us to process continuous audio data to detect and classify bird sounds. This technology can assist researchers in monitoring bird populations' status and trends and ecosystems' 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:听鸟声比看鸟儿更容易。然而,鸟儿在自然中仍然起着重要作用,是环境质量和污染恶化的极好指标。机器学习和进化神经网络最近的进展使我们能够处理连续的音频数据,以探测和分类鸟类声音。这种技术可以帮助研究人员监测鸟类种群的状况和趋势以及生态系统的生物多样性。我们提出一个健全的探测和分类管道,以分析复杂的声谱记录,并查明背景中的鸟叫声。我们的方法从薄弱的标签和很少的数据中学习,并声学上承认鸟类物种。我们的解决办法是,在卡格莱2022 BirdCLEF 挑战中,有807个团队,位于第18位。