论文标题
一种基于软件的方法，用于具有多台操作机器的建筑工作网站的声学建模
A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines
论文作者
论文摘要
已经进行了几项研究，以使用其生成的声音模式自动识别建筑设备的活动。这些研究中的大多数都集中在受控环境下的单机场景上。但是，真正的建筑工作站点更为复杂，通常由几种类型的设备组成，这些设备具有不同的方向，方向和位置。根据使用麦克风阵列（即，在特定的几何布局下安装在板上安装的几个单个麦克风），以及用于对每台机器分类的磁性原理的当前研究目前的识别工作站点上多台机器的活动是针对硬件的。虽然有效，但常见的硬件处理有局限性，并且在普通工作网站上使用麦克风阵列并不总是可行的选择。在本文中，作者提出了一种以软件为导向的方法，该方法使用深神经网络（DNN）和时频面膜（TFMS）来解决此问题。提出的方法需要使用单个麦克风，因为可以通过训练DNN来区分声源。在模拟的工作现场条件下，已对提出的方法进行了测试和验证，在模拟的工作现场条件下，两台机器同时运行。结果表明，软TFM的平均准确性比二进制TFM高38％。
Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations, directions, and locations working simultaneously. The current state-of-research for recognizing activities of multiple machines on a job site is hardware-oriented, on the basis of using microphone arrays (i.e., several single microphones installed on a board under specific geometric layout) and beamforming principles for classifying sound directions for each machine. While effective, the common hardware-approach has limitations and using microphone arrays is not always a feasible option at ordinary job sites. In this paper, the authors proposed a software-oriented approach using Deep Neural Networks (DNNs) and Time-Frequency Masks (TFMs) to address this issue. The proposed method requires using single microphones, as the sound sources could be differentiated by training a DNN. The presented approach has been tested and validated under simulated job site conditions where two machines operated simultaneously. Results show that the average accuracy for soft TFM is 38% higher than binary TFM.
