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

• URL: http://arxiv.org/abs/2309.03544v1
• Date: Thu, 7 Sep 2023 08:02:57 GMT
• Title: MVD:A Novel Methodology and Dataset for Acoustic Vehicle Type Classification
• Authors: Mohd Ashhad, Omar Ahmed, Sooraj K. Ambat, Zeeshan Ali Haq, Mansaf Alam
• Abstract summary: We present two open datasets for the development of acoustic traffic monitoring and vehicle-type classification algorithms. 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.
• Score: 0.6249768559720122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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