论文标题
基于神经网络的声载汽车计数
Neural Network-based Acoustic Vehicle Counting
论文作者
论文摘要
本文解决了使用单渠道音频计数的声学车辆计数。我们预测了从剪裁的车辆到微孔距离的局部最小值的车辆瞬间通过。使用两个阶段（粗细）回归从音频预测此距离，这两个阶段都通过神经网络（NNS）实现。实验表明，基于NN的距离回归的表现超过了先前提出的支持向量回归。 $ 95 \％$ $的置信区间的车辆计数误差的平均值在$ [0.28 \％，-0.55 \％] $之内。除了基于最小值的计数外，我们还提出了一个深度学习计数，该计数在预测的距离上运行，而无需检测到局部最小值。尽管以前的方法表现出色，但深度计数具有显着优势，因为它不取决于最小检测参数。结果还表明，删除功能中低频可以提高计数性能。
This paper addresses acoustic vehicle counting using one-channel audio. We predict the pass-by instants of vehicles from local minima of clipped vehicle-to-microphone distance. This distance is predicted from audio using a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs). Experiments show that the NN-based distance regression outperforms by far the previously proposed support vector regression. The $ 95\% $ confidence interval for the mean of vehicle counting error is within $[0.28\%, -0.55\%]$. Besides the minima-based counting, we propose a deep learning counting that operates on the predicted distance without detecting local minima. Although outperformed in accuracy by the former approach, deep counting has a significant advantage in that it does not depend on minima detection parameters. Results also show that removing low frequencies in features improves the counting performance.
