论文标题
洪布Zooniverse：众群体的声学蚊子数据集
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
论文作者
论文摘要
蚊子是唯一已知的疟疾载体，每年导致数十万人死亡。了解潜在蚊子向量的数量和位置对于有助于减少疟疾传播病例至关重要。近年来，深度学习已被广泛用于生物声学分类任务。为了在该领域启用进一步的研究应用程序，我们发布了新的蚊子录音数据集。我们获得了超过一千个贡献者，获得了195,434个标签，分别为两个持续时间，其中约10％表示蚊子事件。我们介绍了数据集的示例使用，其中我们在该数据集中训练了汇总的神经网络，以log-mel功能上展示了标签的信息内容。我们希望这将成为研究疟疾各个方面的人的重要资源，并添加到现有的音频数据集中进行生物声检测和信号处理。
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year. Understanding the number and location of potential mosquito vectors is of paramount importance to aid the reduction of malaria transmission cases. In recent years, deep learning has become widely used for bioacoustic classification tasks. In order to enable further research applications in this field, we release a new dataset of mosquito audio recordings. With over a thousand contributors, we obtained 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events. We present an example use of the dataset, in which we train a convolutional neural network on log-Mel features, showcasing the information content of the labels. We hope this will become a vital resource for those researching all aspects of malaria, and add to the existing audio datasets for bioacoustic detection and signal processing.
