###### Abstract
Through the application of knowledge and feature distillation techniques, the CCBA model efficiently learns from the dual-stream ResNet50-CNN-LSTM classifier that utilizes SPWVD-MFCC features, resulting in a significant reduction in computational overhead while maintaining high classification accuracy and robustness against noise. This optimization yields marked improvements in processing speed and computational efficiency, making the CCBA model particularly well-suited for edge-based deployments in urban sound monitoring. To evaluate its practical performance, we deployed the CCBA classifier with the HWASN system in real-world urban environments. Our results demonstrated excellent network stability, with no packet loss observed during a 15-minute test period. After fine-tuning the CCBA model with approximately 30 min of real-world data, it demonstrated excellent classification performance, confirming its robustness and practicality for urban acoustic monitoring. Furthermore, by integrating additional environmental parameters such as air quality, light intensity, and temperature, the system's utility was significantly enhanced, transforming it into a comprehensive urban environmental monitoring solution. This multimodal data collection approach further increases the system's practicality in urban monitoring and provides more extensive support for critical applications such as noise pollution monitoring, urban planning, public safety, and environmental assessment. The key contributions of this study are as follows:  
* Real-world deployment of the Hierarchical Wireless Acoustic Sensor Network (HWASN): The network was successfully deployed for urban noise monitoring, with no packet loss observed during real-world testing, verifying its reliable performance. This highlights the system's robustness and scalability for large-scale urban monitoring;
* Development of the CNN-CNN-BiLSTM-Attention (CCBA) model: The end-to-end model was designed using two-stage knowledge and feature distillation techniques,enabling efficient and real-time sound classification on resource-constrained edge devices. This optimization significantly reduces computational overhead while maintaining high classification accuracy, making it ideal for real-time urban monitoring;
* Integration and validation of HWASN with the fine-tuned CCBA model in a case study: The combined system was physically validated in real-world urban environments, demonstrating strong robustness and effectiveness in practical noise monitoring applications. This suggests that the system could be highly effective in diverse urban settings, such as smart city infrastructure and environmental monitoring;
* Incorporation of environmental parameters: Factors such as air quality, light intensity, and temperature were integrated into the system, enhancing its utility for urban planning, public safety, and overall improvements in quality of life. This multimodal approach provides a more comprehensive solution for citywide environmental assessments. The rest of this paper is organized as follows: Section 2 presents the implementation of the HWASN for deployment. Section 3 comprehensively introduces the proposed end-to-end CCBA model, including the datasets, training methods, and performance evaluations. Section 4 discusses a case study involving a real-world deployment, along with the results and application prospects. Section 5 outlines the limitations of this study. Finally, Section 6 concludes the paper.