# Mel-spectrogram features for acoustic vehicle detection and speed estimation

arXiv: https://arxiv.org/abs/2204.04013 · HTML: https://mlsanity.com/paper/2204.04013/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey](https://mlsanity.com/paper/1910.04656/) (2019)
- [Robust Audio-Based Vehicle Counting in Low-to-Moderate Traffic Flow](https://mlsanity.com/paper/2010.11716/) (2020)
- [Neural Network-based Acoustic Vehicle Counting](https://mlsanity.com/paper/2010.11659/) (2020)

## Built on by (most cited first, 2 of 2)

- [A dataset for audio-video based vehicle speed estimation](https://mlsanity.com/paper/2212.01651/) (2022)
- [EMMA: Extracting Multiple physical parameters from Multimodal Data](https://mlsanity.com/paper/2605.24047/) (2026)

## Abstract (arXiv)

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.