## 2 Related Work
The dataset is recorded using multiple audio devices (e.g., high-end microphones and smartphones), improving model cross-device generalization capability. The MAVD [11], developed by the Faculty of Engineering at the University of the Republic in Uruguay, provides synchronized audio-visual recordings captured using a SONY PCM-D50 (Sony, Tokyo, Japan) and a GoPro Hero 3 (GoPro, San Mateo, CA, USA). The audio is segmented into 5 min clips to preserve temporal context in urban traffic scenarios, supporting research in autonomous driving perception and urban noise monitoring. Table 1 provides a summary of these datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Year** & **Total Samples** & **Clip Length** & **Classes** \\ \hline ESC-50 [7] & 2015 & 2000 & 5 s & 50 \\ UrbanSound8K [8] & 2014 & 8732 & 0.3–4.0 s & 10 \\ AudioSet [23] & 2017 & 2,084,320 & 10 s & 532 \\ MAVD [11] & 2019 & 47 & 5 min & 12 \\ TAU Scenes 2020 [24] & 2020 & 5892 & 10 s & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of environmental sound classification datasets. ESC-50 and UrbanSound8K, as high-quality, manually labeled FreeSound datasets, perform well in small-scale environmental sound classification tasks but are limited by small dataset sizes and restricted category diversity, making them insufficient for large-scale urban noise classification. AudioSet, as a large-scale YouTube-based dataset, offers a broader range of urban noise categories but suffers from annotation inaccuracies and potential data unavailability. Although TAU and MAVD utilize diverse recording devices to improve generalization, they are geographically constrained, limiting their scalability for broader applications. These limitations indicate that urban noise classification still lacks a standardized dataset that is large-scale, well-annotated, and class-balanced. Therefore, there is an urgent need to construct a high-quality urban noise dataset that encompasses a wider range of noise categories, maintains precise annotations, and ensures broad applicability, thereby better supporting urban noise monitoring and classification tasks. With the continued expansion of annotated datasets, ESC research has shifted from coarse-grained acoustic scene recognition toward fine-grained classification of specific sound events. Early systems predominantly relied on handcrafted acoustic features combined with shallow classifiers--such as KNN, GMM, and SVM [9; 14]--which demonstrated limited robustness under real-world acoustic conditions. A major milestone was achieved when [PERSON] [15] first introduced CNN to ESC, enabling end-to-end learning of temporal-frequency patterns from spectrograms. SoundNet [16] further addressed the issue of data scarcity by leveraging cross-modal transfer learning from the visual domain, thereby enhancing audio feature representation. Subsequent architectural advancements include the 34-layer ResNet used by [PERSON] and [PERSON] [17] for classifying human indoor activities and the hybrid CNN-LSTM model proposed by [PERSON] and [PERSON] [18] for frog species classification, which also investigated the impact of window size and overlap settings. In comparative studies, [PERSON] and [PERSON] [25] demonstrated that a Long Short-Term Memory (LSTM) model trained on MEL spectrograms achieved an accuracy of 90.15%, outperforming standard CNN architectures, while Deep Neural Network (DNN) models based on Mel-Frequency Cepstral Coefficients (MFCCs) exhibited more stable performance. [PERSON] et al. [26] introduced a multi-branch model combining LSTM, Gated Recurrent Unit (GRU), Bi-directional LSTM (Bi-LSTM), and Bi-directional GRU (Bi-GRU), achieving further improvements on the UrbanSound8K dataset. In recent years, attention mechanisms have been increasingly incorporated into CNN-based frameworks to improve the selectivity of acoustic feature extraction. The Squeeze-and-Excitation (SE) module [27] utilizes global average pooling followed by a two-layer bottleneck to dynamically recalibrate channel-wise feature responses.