An urban environmental noise source identification model based on parallel deep learning network with convolutional block attention
Xiaodan Hong, Haomiao Nie, Wenying Zhu · Journal of low frequency noise, vibration and active control · 2025
With the rapid development of urbanization, noise pollution has become a serious environmental issue affecting human health and quality of life. Timely acquisition of accurate information about noise sources is crucial for efficient and precise management and control of regional environmental noise pollution. However, traditional methods that rely on manual offline identification of noise sources are not only time-consuming and labor-intensive, but also the results have often lack timeliness. In this study, for real-time and automatically identifying the categories of environmental noise sources in urban areas, a deep convolutional recurrent neural network (DCRNN) based on the Convolutional Block Attention Module (i.e., the parallel CBAM-DCRNN model) was developed by studying different integration strategies. To enhance generalizability of the proposed model, a heavyweight urban environmental noise dataset encompassing 20 typical categories (totaling 13,654 labeled samples) was collected, which includes various spectral features of environmental noises. And also, the transfer learning method was introduced to further enhance the model’s training efficiency and also improve its scalability to larger datasets. As a result, the 92.63% accuracy validated the satisfactory performance of the proposed identification model in a large urban environmental noise dataset, significantly outperforming the classical DCRNN model, even for categories with few training data. Moreover, the experiment validated that the identification effect of the proposed parallel integrated model is significantly superior to this of the CBAM-DCRNN model using sequential integration strategy. The proposed model can be applied to design an environmental noise online automatic monitoring and identification instrument, for real-time automatic identification and early warning of environmental noise pollution sources in noise-sensitive urban areas.