A traffic noise source identification method for buildings adjacent to multiple transport infrastructures based on deep learning

In recent years, the rapid construction of roads and railways in cities has brought convenience to urban residents, but the noise generated by the functioning of these transport infrastructures also affects the normal life of residents in the buildings around them. Buildings adjacent to both roads and railways are exposed to multiple traffic noises at the same time, which presents difficulties for targeted noise assessment and control. To solve this problem, this paper proposes a deep learning-based traffic noise source identification method, which feeds the time-frequency spectrum of the noise or vibration monitoring signals into the trained deep learning based model, so that the sources of noise at various moments can be identified. In addition, the performance of the proposed method was presented using noise and vibration signals monitored in Beijing, and noise evaluations for two buildings were conducted as case studies to investigate the statistical characteristics of noise caused by rail and road traffic in buildings. The result demonstrates the feasibility of the proposed method, and shows that different types of noise pollution need to be attended to on different floors in buildings adjacent to underground railways, the lower floors of the building are exposed to more significant low-frequency structural noise induced by the underground railway, while the higher floors of the building are exposed to more significant airborne noise caused by road traffic. Also, extensive areas adjacent to elevated railways are affected by airborne noise, and buildings in these areas require attention to the prevention of noise.