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.

翻译：本文用单声道音频计算音响飞行器。 我们从车到麦克风距离的当地小微米中预测车辆路过瞬时。 使用两阶段( 粗微) 回归从音频中预测距离, 两阶段都通过神经网络( NNS) 实现。 实验显示, NN 的距离回归远比先前提议的支持矢量回归远。 车辆计时误差的置信区间为$95 美元。 除了基于小数的计时外, 我们建议对预测距离进行深度的计算,而不探测当地迷你。 尽管前一方法的准确性高于前一方法, 深度计数有很大的优势在于它不依赖于迷你检测参数。 结果还显示, 去除低频率的功能提高了计数的性能。