## 1 Introduction
Despite recent progress, ESC continues to face significant challenges in both dataset construction and model development. Manual annotation of audio data remains time-consuming and highly subjective, particularly in scenarios involving overlapping sources or ambiguous acoustic boundaries. Studies indicate that the average time required for annotating audio is three to five times greater than for image data [21]. Furthermore, although mainstream datasets such as ESC-50 and UrbanSound8K contain diverse urban audio clips, they lack noise types that are both persistent and commonly perceived as disturbing, such as traffic, construction, and crowd noise. In contrast, gunshots and coughing are typically transient, while animal sounds like sheep bleats are rarely encountered in real urban settings. Compounding this, the acoustic profiles of urban environments vary significantly across regions, making it difficult to balance representativeness and diversity when constructing datasets [22]. From a modeling standpoint, many urban noise categories share highly similar temporal-frequency structures, while intra-class variability--caused by factors such as reverberation, background interference, and device heterogeneity--further exacerbatesthe challenge of accurate classification. Existing approaches often struggle to achieve robust feature representation and generalization across diverse recording conditions. To address these limitations, we introduce a new urban noise benchmark dataset (UN15) and propose a convolutional neural network architecture that combines a residual backbone with a temporal-frequency attention mechanism (ResNet-TF). The UN15 dataset comprises 1620 audio clips from 15 representative and commonly disruptive urban sound categories--covering traffic, construction, commercial activity, public gatherings, and industrial operations. The majority of the audio samples were sourced from the Freesound online database, complemented by additional clips extracted from YouTube videos and a smaller portion recorded directly using mobile and handheld devices. Each clip lasts 5 s and was carefully segmented and annotated by researchers to ensure labeling accuracy and content quality. The dataset spans diverse locations, times, and recording conditions, enhancing its realism and variability. All samples follow a unified segmentation length, sampling rate, and acoustic preprocessing pipeline, ensuring compatibility with supervised learning workflows. On the modeling side, we propose ResNet-TF, a model built upon the ResNet backbone and enhanced with a TF attention mechanism. This module jointly captures salient temporal segments and spectral regions within the audio signal, improving the model's sensitivity to informative features. Compared to conventional CNN or RNN architectures, ResNet-TF demonstrates superior capability in modeling temporal dependencies and local spectral variations, offering enhanced robustness and generalization across diverse soundscapes. The main contributions of this work are summarized as follows:  
* We construct and release a high-quality urban noise dataset covering 15 typical classes, with comprehensive documentation on sound category selection, annotation procedures, and preprocessing. * We propose a ResNet-based architecture enhanced with a TF attention mechanism, improving feature representation under complex acoustic conditions. * Finally, we conduct extensive comparative experiments between the UN15 dataset and existing publicly available datasets, providing an in-depth discussion of the experimental outcomes. The remainder of this paper is organized as follows. Section 2 reviews recent work on environmental sound classification and publicly available urban noise datasets. Section 3 introduces the construction process and characteristics of the proposed UN15 dataset. Section 4 describes the feature extraction process and the architecture of the ResNet-TF model. Section 5 presents the experimental setup, evaluation metrics, and results.