# MVD:A Novel Methodology and Dataset for Acoustic Vehicle Type Classification

arXiv: https://arxiv.org/abs/2309.03544 · HTML: https://mlsanity.com/paper/2309.03544/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 2 of 2)

- [IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research](https://mlsanity.com/paper/2104.13620/) (2021)
- [Improved Vehicle Sub-type Classification for Acoustic Traffic Monitoring](https://mlsanity.com/paper/2302.02945/) (2023)

## Built on by (most cited first, 1 of 1)

- [Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails](https://mlsanity.com/paper/2608.14952/) (2026)

## Abstract (arXiv)

Rising urban populations have led to a surge in vehicle use and made traffic monitoring and management indispensable. Acoustic traffic monitoring (ATM) offers a cost-effective and efficient alternative to more computationally expensive methods of monitoring traffic such as those involving computer vision technologies. In this paper, we present MVD and MVDA: two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms, which contain audio recordings of moving vehicles. The dataset contain four classes- Trucks, Cars, Motorbikes, and a No-vehicle class. Additionally, we propose a novel and efficient way to accurately classify these acoustic signals using cepstrum and spectrum based local and global audio features, and a multi-input neural network. Experimental results show that our methodology improves upon the established baselines of previous works and achieves an accuracy of 91.98% and 96.66% on MVD and MVDA Datasets, respectively. Finally, the proposed model was deployed through an Android application to make it accessible for testing and demonstrate its efficacy.