In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic flow in order to to support municipal traffic planning decisions. In this paper, we present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices with restricted microphone quality and hardware processing power. In addition, this paper provides a detailed review of recent acoustic traffic monitoring (ATM) algorithms as well as the results of two benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art convolutional neural network architectures.

中文翻译：

---

IDMT-Traffic：用于声音交通监控研究的开放基准数据集

在许多城市地区，交通负荷和噪声污染都在不断增加。自动化的交通监控系统是有前途的对策，可以对地方交通流量进行系统地量化和预测，以支持市政交通规划决策。在本文中，我们提供了一个新颖的开放基准数据集，其中包含2.5小时的立体声音频记录，这些音频记录了使用高质量sE8和中等质量MEMS麦克风捕获的4718个车辆通过事件。此数据集非常适合评估在麦克风质量和硬件处理能力受到限制的情况下将音频分类算法部署到嵌入式传感器设备的用例。此外，