## 5 Audio Event-Based Classification  
In environmental assessments, auditory analysis is a relatively new field compared to visual methods. Historically, urban sounds were often regarded as nuisances, primarily studied in the context of noise pollution [29]. Significant research has focused on developing outdoor noise measurement and reduction techniques [30]. The detrimental effects of noise from vehicles and industrial machinery on physical and mental well-being are well documented [31]. Traditionally, sound assessments relied on sound level meters, simplifying complex urban soundscapes into mere decibel measurements. Recent years, however, have seen a paradigm shift towards evaluating auditory scenes, known as Soundscapes. [PERSON] [32] argued that soundscape evaluations consider not only the physical aspects of sound but also the subjective human perceptions. This shift has spurred growth in soundscape research, emphasizing the need for sophisticated management of sonic environments [33]. Soundscape evaluation methodologies typically include in situ surveys by observers to capture auditory perceptions at specific locations [34; 35], and the use of audio recordings from handheld recorders and binaural microphones for off-site response assessments [36; 37]. Innovative studies are employing technologies like head-mounted displays and virtual reality to explore urban environments, focusing on aesthetic preferences [38] and overall satisfaction [39], though such methods have limited scope in urban settings [40], which may impact the generalizability of results. The challenge remains in developing automated methods for extracting sound sources from recordings within the dynamic, unstructured urban contexts, which hampers the scalability of soundscape studies over larger areas [41]. Nonetheless, understanding the diverse sound sources in urban areas, such as sidewalks and pedestrian routes, is crucial for creating context-aware autonomous systems. These systems enhance safety in dynamic pedestrian environments by leveraging rich auditory data, thus addressing current technological limitations. To enhance our understanding of the acoustic environment within the DELTA dataset, particularly in pedestrian pathways, we integrated automated audio event classification by employing deep learning models. This initiative aimed to categorize the diverse soundscapes recorded. Specifically, we utilized two prominent deep learning models for this task: YAMNet [42] and YOLOv8 [43]. YAMNet, pre-trained on Google's comprehensive  
Figure 7: Aligned audio waveforms from an action camera (**top**) and a Tascam recorder (**bottom**), demonstrating synchronization over a 5 s interval with corresponding time (s) and amplitude variations. AudioSet [44], provides a solid analytical foundation for our study. The extensive range and meticulous categorization of audio events in AudioSet were instrumental in preparing YAMNet to accurately identify a wide array of sounds. This capability aligns perfectly with the objectives of our research, allowing us to deepen our insights into the acoustic landscape captured in our dataset. Utilizing the YAMNet architecture, which integrates the MobileNet-v1 [45] design known for its depthwise-separable convolutions, our study delved into classifying audio event classes from a curated collection of audio segments. This began with normalizing the amplitude range of the audio files, creating a uniform base for analysis. We aligned YAMNet's parameters, such as sampling rate and frame hop time, with our audio signals' characteristics to ensure precise temporal representation. To comply with YAMNet's input standards, stereo audio files were converted into mono format by averaging the channels. Following this preprocessing, each audio segment underwent analysis via YAMNet, which produced detailed spectrograms and class activation maps for each segment.