用于鸟类音频检测的密集连接CNN
Densely Connected CNNs for Bird Audio Detection
摘要：
自动检测音频记录中的鸟类声音，如果足够准确，预计将对生物和生态声学研究界有很大的帮助，这些研究界对基于音频现场记录监测生物多样性感兴趣。为了评估最先进的机器学习方法的准确性，最近组织了涉及大型音频数据集的鸟类音频检测挑战。在本文中，使用几种类型的卷积神经网络（即标准CNN，残差网络和密集连接网络）的实验报告在这个挑战的框架。DenseNets是首选解决方案，因为它们是性能最佳和最紧凑的模型，在挑战的测试集上获得了88.22%的受试者操作曲线下面积。通过时间和频率偏移、训练期间的模型参数平均和使用几何平均值的集合方法进行数据增强，获得了性能增益。相反，试图用测试集的样本来扩大训练数据集，并将自动预测用作伪地面实况标签，这会不断降低性能。
英文摘要：
Detecting bird sounds in audio recordings automatically, if accurate enough, is expected to be of great help to the research community working in bio- and ecoacoustics, interested in monitoring biodiversity based on audio field recordings. To estimate how accurate the state-of-the-art machine learning approaches are, the Bird Audio Detection challenge involving large audio datasets was recently organized. In this paper, experiments using several types of convolutional neural networks (i.e. standard CNNs, residual nets and densely connected nets) are reported in the framework of this challenge. DenseNets were the preferred solution since they were the best performing and most compact models, leading to a 88.22% area under the receiver operator curve score on the test set of the challenge. Performance gains were obtained thank to data augmentation through time and frequency shifting, model parameter averaging during training and ensemble methods using the geometric mean. On the contrary, the attempts to enlarge the training dataset with samples of the test set with automatic predictions used as pseudo-groundtruth labels consistently degraded performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
