基于卷积神经网络的三维空间声源方向检测

Xiaofeng Yue, Guangzhi Qu, Bo Liu, Anyi Liu
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引用次数: 3

摘要

声源检测和定位在许多工业环境中有许多实际用途。文献中大多数声源方向检测算法都是为了识别二维空间中声源的角度。在这项工作中,我们提出使用卷积神经网络来检测三维空间中的声源方向。该算法基于相位变换广义互相关法(GCC-PHAT)[1]推导到达时延(TDOA)。通过使用卷积神经网络模型,可以实现该算法的应用和部署。此外,通过修改GCC-PHAT公式,该方法也适用于多声源检测。单声源和多声源检测的仿真实验结果表明,该系统在大多数情况下都能正常工作。
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Detection Sound Source Direction in 3D Space Using Convolutional Neural Networks
Sound source detection and localization have a lot of practical uses in many industrial settings. Most of sound source direction detection algorithms in literature are designed to identify the angle of sound source in a 2D space. In this work, we propose to use convolutional neural networks to detect the sound source direction in a 3D space. This algorithm is based on the generalized cross correlation method with phase transform (GCC-PHAT) [1] to derive time delay of arrival (TDOA). By using a convolutional neural network model, this algorithm can be applied and deployed. In addition, by modifying GCC-PHAT formula, this approach also works of multiple sound sources detection. Simulation experimental results on single sound source and multiple sound sources detection show the proposed system could work in most situations.
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