通过音频物联网传感器、调制张量图和循环神经网络改进对蜂群强度的监测
Improved Monitoring of Honey bee Colony Strength via Audio IoT Sensors, Modulation Tensorgrams and Recurrent Neural Networks
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中文总结 AI 辅助
研究利用音频物联网传感器等，通过新调制张量图及卷积神经网络等，对蜂群强度进行监测，相比先前基准方法，在准确性、通用性和鲁棒性上有所提升，还通过相关分析显示调制频谱时间动态的重要性。
中文摘要 AI 辅助
蜜蜂作为农作物和野生植物的关键传粉者，在农业和生态系统稳定中发挥着至关重要的作用。因此，利用物联网传感器远程监测蜂箱强度已成为一项关键任务。此前研究表明，从音频物联网设备的调制频谱中提取的手工特征可改善对蜂群强度的声学监测。本文提出假设，调制频谱的时间动态中存在重要的判别信息，但先前方法会将其丢弃。于是探索使用保留时间维度的新调制张量图，并将其作为卷积神经网络和卷积循环深度神经网络的输入。使用包含3000多小时蜂箱音频记录的公共UrBAN数据集，结果表明该方法在准确性和跨蜂箱通用性方面优于先前的基准方法，对野外嘈杂录音条件的鲁棒性也有所提高。还通过显著性图和梯度加权类激活图进行可解释性分析，显示了调制频谱时间动态对该任务 的重要性。总体而言，研究结果表明对蜂群强度进行准确、通用且鲁棒的声学监测是可行的。
英文摘要
Honey bees (Apis mellifera) play a crucial role in agriculture and ecosystem stability as key pollinators of crops and wild plants. As such, monitoring hive strength remotely with Internet of Things (IoT) sensors has become a crucial task. Previously, handcrafted features extracted from the modulation spectrum of audio IoT devices were shown to improve acoustic monitoring of colony strength. In this paper, we hypothesize that important discriminative information is present in the temporal dynamics of the modulation spectrum, but this information is discarded with prior methods. As such, we explore the use of a new modulation tensorgram where the time dimension is kept. This new representation is used as input to a convolutional neural network (CNN) and a convolutional recurrent deep neural networks (CRDNN). Using the public UrBAN dataset, which contains more than 3,000 hours of beehive audio recordings, we show that the proposed method improves both accuracy and cross-hive generalizability over prior benchmark methods, and the results further suggest improved robustness to noisy in-the-wild recording conditions. We use saliency maps and gradient-weighted class activation maps for explainability and show the importance of the modulation spectral temporal dynamics for the task at hand. Overall, our results suggest that accurate, generalizable, and robust acoustic monitoring of honey bee colony strength is possible.
