Acoustic vehicle speed estimation from single sensor measurements

The paper addresses acoustic vehicle speed estimation using single sensor measurements. We introduce a new speed-dependent feature based on the attenuation of the sound amplitude. The feature is predicted from the audio signal and used as input to a regression model for speed estimation. For this research, we have collected, annotated, and published a dataset of audio-video recordings of single vehicles passing by the camera at a known constant speed. The dataset contains 304 urban-environment real-field recordings of ten different vehicles. The proposed method is trained and tested on the collected dataset. Experiments show that it is able to accurately predict the pass-by instant of a vehicle and to estimate its speed with an average error of 7.39 km/h. When the speed is discretized into intervals of 10 km/h, the proposed method achieves the average accuracy of 53.2% for correct interval prediction and 93.4% when misclassification of one interval is allowed. Experiments also show that sound disturbances, such as wind, severely affect acoustic speed estimation.