  We consider improving the performance of a recently proposed sound-based
vehicle speed estimation method. In the original method, an intermediate
feature, referred to as the modified attenuation (MA), has been proposed for
both vehicle detection and speed estimation. The MA feature maximizes at the
instant of the vehicle's closest point of approach, which represents a training
label extracted from video recording of the vehicle's pass by. In this paper,
we show that the original labeling approach is suboptimal and propose a method
for label correction. The method is tested on the VS10 dataset, which contains
304 audio-video recordings of ten different vehicles. The results show that the
proposed label correction method reduces average speed estimation error from
7.39 km/h to 6.92 km/h. If the speed is discretized into 10 km/h classes, the
accuracy of correct class prediction is improved from 53.2% to 53.8%, whereas
when tolerance of one class offset is allowed, accuracy is improved from 93.4%
to 94.3%.
