基于多视角CNN的动物种类自动识别声学分类系统
A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
摘要：
通过动物的叫声自动识别动物种类是一项重要而具有挑战性的任务。虽然在文献中已经提出了许多种音频监测系统，他们遭受的几个缺点，如非平凡的特征选择，准确性下降，因为环境噪声或密集的本地计算。在本文中，我们提出了一个基于深度学习的无线声学传感器网络（WESTERN）的声学分类框架。所提出的框架是基于云架构，放松对无线传感器节点的计算负担。为了提高识别精度，我们设计了一个多视图卷积神经网络（CNN）并行提取短期，中期和长期的依赖关系。在两个真实数据集上的测试结果表明，当环境噪声占主导地位（低信噪比）时，该结构能够获得较高的分类精度，并且显著优于传统的分类系统。此外，我们实现和部署的测试平台上的系统，并分析在现实世界的环境中的系统性能。仿真和真实世界的评估表明，所提出的声学分类系统在区分动物物种的准确性和鲁棒性。
英文摘要：
Automatic identification of animal species by their vocalization is an important and challenging task. Although many kinds of audio monitoring system have been proposed in the literature, they suffer from several disadvantages such as non-trivial feature selection, accuracy degradation because of environmental noise or intensive local computation. In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN). The proposed framework is based on cloud architecture which relaxes the computational burden on the wireless sensor node. To improve the recognition accuracy, we design a multi-view Convolution Neural Network (CNN) to extract the short-, middle-, and long-term dependencies in parallel. The evaluation on two real datasets shows that the proposed architecture can achieve high accuracy and outperforms traditional classification systems significantly when the environmental noise dominate the audio signal (low SNR). Moreover, we implement and deploy the proposed system on a testbed and analyse the system performance in real-world environments. Both simulation and real-world evaluation demonstrate the accuracy and robustness of the proposed acoustic classification system in distinguishing species of animals.
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。
