## 6 Conclusions  
In this study, we proposed a novel environmental sound classification framework that combines a newly constructed urban noise dataset (UN15) with a temporal-frequency attention-enhanced ResNet architecture (ResNet-TF). The UN15 dataset addresses limitations in previous public datasets--such as limited noise diversity, short recording duration, and lack of real-world scenarios--by incorporating both online samples and carefully curated field recordings. This hybrid construction enables better representation of real-life urban noise, including underrepresented categories such as drones and construction machinery. To validate the effectiveness of the dataset and the proposed model, extensive experiments were conducted on both UN15 and the widely used UrbanSound8K benchmark. The results show that ResNet-TF achieves state-of-the-art performance compared to several baseline models, demonstrating the effectiveness of incorporating decoupled TF attention into residual networks. Furthermore, confusion matrix analysis indicates that TF-Attention helps the model capture discriminative structure in noisy acoustic environments, although certain overlapping classes remain challenging due to spectral similarity. Despite the promising results, some limitations remain. First, although UN15 improves data realism and diversity, the overall scale is still modest (1620 clips across 15 classes), which could increase the risk of overfitting. To address this, we plan to continuously expand UN15 by supplementing publicly sourced data with on-site recordings in varied environments, thus improving class coverage and dataset balance over time. Second, while our model performs well on known categories, its generalization ability to unseen or out-of-domain urban noises is not fully guaranteed. Future work will explore domain adaptation,data augmentation, and weak or self-supervised learning strategies to enhance robustness in low-resource or non-stationary settings. Lastly, we acknowledge that the current ResNet-TF design employs a fixed hierarchical attention structure, which may not optimally capture varying scale or context-dependent features across layers. To overcome this, we intend to investigate dynamic attention placement, multi-scale fusion, and lightweight transformer hybrids that better adapt to the complexity of urban soundscapes. These future directions will strengthen the generalizability, efficiency, and deployment readiness of ESC models in real-world applications such as smart cities, public safety, and environmental monitoring. Conceptualization, Y.S. and G.C. ; methodology, Y.S., G.C. and H.-Y.D. ; formal analysis, Y.S. and B.D. ; data curation, Y.S. ; writing--original draft preparation, Y.S., G.C. and C.-M.L. ; writing--review and editing, C.-M.L. and H.-Y.D. ; supervision, Y.S. and C.-M.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The dataset proposed in this study, UN15, was specifically constructed for urban environmental sound classification. Due to the inclusion of potentially sensitive content--such as public demonstrations and vocal expressions--the dataset is not released for direct public download in order to comply with privacy regulations, copyright restrictions, and ethical considerations. However, restricted access can be provided for academic research upon request. Interested researchers may contact [PERSON] via email at [EMAIL_ADDRESS] to request access and obtain the download link. The authors would like to express their sincere gratitude to [PERSON], HY.D, [PERSON], and [PERSON] for their valuable support and contributions during the construction and validation of the UN15 dataset. The authors also gratefully acknowledge Scien Co., Ltd. for providing essential equipment and financial support throughout the data acquisition and model development processes. The authors declare that there is no conflict of interest or personal relationship. The following abbreviations are used in this manuscript: