Sound Event Detection in Smart Cities: A Systematic Review of Methods, Datasets, and Applications

Sound Event Detection (SED) is a growing area with vast prospects for understanding and designing the sonic fabric of smart cities. In this paper, the latest advances in SED are summarized, focusing on models, datasets, and applications from scientific papers listed on Scopus and Web of Science. The paper provides a clear view of how SED is being used in smart cities, public safety, environment monitoring, and home security. The paper also addresses the challenges of SED, including dataset representativeness, model robustness under noisy or complex acoustic scenes, event rarity detection, as well as the ethics of using automatic listening. The paper also provides a view of future work to be undertaken in SED. The focus of the paper is on self-supervised learning, multi-modal fusion, neuro-inspired approaches, as well as privacy-preserving analytics. The paper provides a view of SED as a key technology to make smart cities safe, secure, and sustainable. SED has vast prospects as a key technology to enable artificial perception of smart cities.