论文标题
MEL-SEXPROGRAMPROMPROMPROMPOR特征，用于声媒体检测和速度估计
Mel-spectrogram features for acoustic vehicle detection and speed estimation
论文作者
论文摘要
该论文介绍了单个传感器测量值的声车检测和速度估计。我们通过最大程度地减少了剪裁的车辆对微孔距离来预测车辆的通行证，这是通过有监督的学习方法预测的，这是从输入音频的旋光光谱图中预测的。此外，基于MEL-Spectrogran图的功能直接用于车速估计，而无需引入任何中间功能。结果表明，所提出的功能可用于准确的车辆检测和速度估计，平均误差为7.87 km/h。如果我们将速度估计作为分类问题（以10 km/h的离散间隔为单位，则提出的方法对于正确的类预测的平均准确性为48.7％，当允许允许一类偏移时，该方法的平均准确性为91.0％。在304个城市环境的现场录音中，评估了所提出的方法。
The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the mel-spectrogram of input audio, in a supervised learning approach. In addition, mel-spectrogram-based features are used directly for vehicle speed estimation, without introducing any intermediate features. The results show that the proposed features can be used for accurate vehicle detection and speed estimation, with an average error of 7.87 km/h. If we formulate speed estimation as a classification problem, with a 10 km/h discretization interval, the proposed method attains the average accuracy of 48.7% for correct class prediction and 91.0% when an offset of one class is allowed. The proposed method is evaluated on a dataset of 304 urban-environment on-field recordings of ten different vehicles.
