## 3 Un15
### Data Collection and Selection  
Existing public datasets for ESC, such as ESC-50 and UrbanSound8K, are primarily constructed from the Freesound audio-sharing platform. While these datasets have played a crucial role in advancing early research, their creation dates back nearly a decade, rendering them increasingly outdated in terms of category definitions and temporal coverage. With the proliferation of electric vehicles, evolving urban infrastructure, and the rise in diverse public activities, both the spectral characteristics of traditional noise sources and the emergence of new urban sounds--such as drones, air conditioners, and public assemblies--have substantially changed. To better reflect the composition and characteristics of contemporary urban noise sources, we constructed a new high-quality dataset, UN15, based on the previously defined class taxonomy. UN15 covers a broad range of high-disruption scenarios (e.g., traffic, construction, crowd events) and includes several important categories absent from prior datasets, such as Air Raid Siren, Drone, and Fireworks. As shown in Figure 2, the proposed dataset offers improved real-world relevance and category completeness, thereby enhancing its ability to model complex urban soundscapes. To ensure both acoustic representativeness and temporal relevance, we employed a hybrid data acquisition strategy: in addition to re-scraping audio samples from Freesound and YouTube, we conducted field recordings in multiple urban settings to capture the authentic acoustic properties of newly emerging sound sources. In total, over 4000 raw audio-video segments (spanning more than 50 h) were collected. These samples were initially filtered using user-provided metadata, followed by manual inspection to eliminate clips with excessive background noise or ambiguous labels, ensuring high annotation quality and signal fidelity. To ensure the acoustic fidelity and structural consistency of field-recorded audio samples, we adopted a standardized recording setup and deployment configuration across all recording sessions. The system consisted of an MSI laptop, a SCIEN 3241 digital-to  
Figure 1: Workflow of the UN15 dataset construction, including audio collection, preprocessing, and manual annotation. analog converter, and a Scien CM-73 directional microphone, which offers a broad dynamic range and flat frequency response, making it suitable for capturing environmental sounds in urban settings. All recordings were conducted at a sampling rate of 44.1 kHz and 24-bit resolution, ensuring high-fidelity audio quality. Figure 3a illustrates the recording system deployed in an open roadside environment, while Figure 3b shows the same setup positioned near a mechanical noise source (air conditioning unit), highlighting its adaptability to diverse acoustic scenes during field recording. For approximately 12.3% of the dataset, which was self-recorded, we followed a data acquisition protocol aligned with ISO 1996-2:2017 [31]. Although our study does not involve the physical measurement of sound pressure level, we adhered to several recommended practices from this standard to ensure recording consistency and reproducibility. Specifically, the microphone was mounted on a tripod at approximately 1.5 m above ground level--within the 1.2-1.5 m range recommended to simulate the typical human ear height (Section 6.2 of the ISO standard). A minimum distance of 1 m was maintained between the microphone and any walls or reflective surfaces to reduce interference from reflections (Section 6 of the ISO standard). Recording sites were chosen in semi-open urban environments such as streets, parks, and squares, consistent with the standard's guidance for realistic environmental noise sampling (Section 5 of the ISO standard). Figure 3: Representative field recording setups used for constructing the UN15 dataset. **(a)** Roadside configuration with directional microphone, audio interface, and laptop for capturing traffic-related sounds.