  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.
