# An approach to improving sound-based vehicle speed estimation

arXiv: https://arxiv.org/abs/2204.05082 · HTML: https://mlsanity.com/paper/2204.05082/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Learning from Noisy Labels with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2007.08199/) (2020)
- [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, 3 of 3)

- [A dataset for audio-video based vehicle speed estimation](https://mlsanity.com/paper/2212.01651/) (2022)
- [Adaptive Vehicle Speed Classification via BMCNN with Reinforcement Learning-Enhanced Acoustic Processing](https://mlsanity.com/paper/2509.00839/) (2025)
- [EMMA: Extracting Multiple physical parameters from Multimodal Data](https://mlsanity.com/paper/2605.24047/) (2026)

## Abstract (arXiv)

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%.