"Seeing Sound": Audio Classification with the Wigner-Wille Distribution and Convolutional Neural Networks

TL;DR

This paper explores classifying urban sound data using the Wigner-Ville distribution combined with CNNs, demonstrating promising results on open-source datasets for sensor-based urban safety applications.

Contribution

It introduces a novel method combining Wigner-Ville distribution with CNNs for sound classification in urban environments, advancing sensor data analysis techniques.

Findings

Effective sound classification on open-source datasets

Potential for urban safety and alert systems

Foundation for real-world application development

Abstract

With big data becoming increasingly available, IoT hardware becoming widely adopted, and AI capabilities becoming more powerful, organizations are continuously investing in sensing. Data coming from sensor networks are currently combined with sensor fusion and AI algorithms to drive innovation in fields such as self-driving cars. Data from these sensors can be utilized in numerous use cases, including alerts in safety systems of urban settings, for events such as gun shots and explosions. Moreover, diverse types of sensors, such as sound sensors, can be utilized in low-light conditions or at locations where a camera is not available. This paper investigates the potential of the utilization of sound-sensor data in an urban context. Technically, we propose a novel approach of classifying sound data using the Wigner-Ville distribution and Convolutional Neural Networks. In this paper, we report on the performance of the approach on open-source datasets. The concept and work presented is based on my doctoral thesis, which was performed as part of the Engineering Doctorate program in Data Science at the University of Eindhoven, in collaboration with the Dutch National Police. Additional work on real-world datasets was performed during the thesis, which are not presented here due to confidentiality.