Advancing Audio Surveillance in Simulated Environments: Real-World Soundscapes and Targeted Noise Detection through Enhanced Beamforming Techniques

Abstract

This paper introduces an innovative beamforming approach designed for audio surveillance, executed through a virtual simulation of a real-world environment based at Liverpool John Moores University. Our research is driven by the increasing requirement for sophisticated audio analysis methods to isolate and enhance specific sounds within noisy environments for forensic analysis, for example, in criminal court cases. By leveraging a time-delay beamforming algorithm, our work offers a novel solution to discern and amplify targeted noises amidst complex soundscapes, a challenge commonly encountered in urban surveillance and forensic audio analysis. Our approach's foundation lies in utilising a carefully arranged, robust array of omnidirectional microphones, which are instrumental in capturing a wide range of real-world sound signals. The core of our methodology involves processing captured sounds using the proposed algorithm, followed by evaluating the system's effectiveness in capturing the desired localised audio sources. This paper explores the system's resilience against microphone array degradation, showcasing its robustness in scenarios of partial system functionality. The experiments, grounded in the simulation of real-world acoustic environments, demonstrate the algorithm's adeptness at managing sound reflections and reverberation, critical factors in the realistic replication of urban soundscapes. It also considers the broader implications of our findings, exploring the potential for adopting this technology in various domains beyond law enforcement, including broadcast solutions, advanced audio engineering applications, and animal conservation in the wild. In conclusion, this research showcases a creative approach to audio surveillance and opens the door to numerous applications that can benefit from enhanced methods of audio isolation and analysis. Our findings contribute to the ongoing discourse on developing advanced surveillance technologies, offering insights that could help shape the future of audio processing and analysis.