The experience of interior space is profoundly shaped by the interplay of various sensory inputs. Despite this, soundscape design remains an underexplored avenue for enhancing spatial experience in built environments. Using virtual reality technology, this study simulated how musical soundscape, including objective musical attributes (mode, tempo, reverberation time—RT), and subjective music evaluations, affect spatial perception and roaming behavior within exhibition spaces featuring diverse layouts. The key findings are as follows: (1) The explanatory contributions of music attributes and subjective evaluations vary across different spatial perceptual dimensions. For instance, major mode and slow tempo significantly enhance spatial scale perception, whereas music familiarity plays a role in influencing spatial legibility. (2) Visual factors have an inherent effect on spatial perception, with significant impacts on spatial scale, fluidity, abundance, and legibility. (3) Music influences spatial perception across different layouts, and its mediating effects on roaming behavior differ. For instance, in axial spaces, increasing spatial abundance positively impacts roaming time and range, with RT conditions playing a key role. In shape-contrast spaces, spatial legibility significantly influences roaming range, with mode and familiarity affecting the results. This study provides a comprehensive exploration of the complex interactions between music, spatial perception, and human behavior, offering valuable insights for future soundscape design.

The concept of a soundmark refers to a distinctive sound source that identifies a particular area based on its environmental acoustic characteristics. This study investigated the factors and processes of soundmark identification in urban parks. After interviewing participants in 14 different urban parks in China, six categories that affect soundmark identification were identified, "sound source", "context", "sound perception", "individual features", "response" and "comprehension". First, the core category is context. As for context, in amusement parks, activity parks, and nature parks, respectively, the theme, function, and scene affect the soundmark identification. For sound perception, soundmark identification is affected by the occurrence of birdsongs and the duration of water sounds in nature parks. Soundmark identification is affected by contrast, duration, and audio-visuals in amusement parks and activity parks. Furthermore, a soundmark identification model specifically for urban parks has been established. The model contains two identified processes: responsive soundmarks and comprehensive soundmarks. For responsive soundmark identification processes, sound source, context, sound perception, and response are essential factors. For comprehensive soundmark identification processes, sound source, context, individual features, and comprehension are essential factors. The soundmarks cannot be identified if the response or comprehension is missed during the process. Ultimately, the park's cultural features promote the identification of comprehensive soundmarks. In addition, soundmarks can be effectively transformed by changing the context of urban parks or the characteristics of sound perception.

Existing soundscape prediction model application conditions and calculation processes are very complex, and difficult to combine with design processes. Therefore, in this study, a visual soundscape prediction model is constructed for sound pressure level (SPL) prediction, sound source prediction, and soundscape evaluation prediction by inputting the elements of urban design under the meshing method (grid 50 m) into a machine learning Gaussian mixture model (GMM). Taking three typical urban parks as examples, the soundwalk method is used to collect subjective perception information on-site to verify prediction accuracy. The results show that by applying geographic information data, including the minimum distance values from predicted points to roads, entrances and exits, and internal nodes, the SPL can be predicted. When the accuracy rate is stable within a 3 dBA error range, the prediction accuracy rate is 67.7%. The visual perception information added is used to quickly predict the sound source type, and the output is the visualized distribution of the natural, human, and mechanical sound perception with a 77.4% accuracy rate. Finally, combining geographic, visual and sound data to predict soundscape evaluation, the model's output are good, medium, and poor categories for each descriptor of the soundscape evaluation, and the prediction results are visualized in three colours with a 74.2% accuracy rate. The convenient soundscape prediction model proposed in this paper can be applied to design practice. By adjusting park design elements, the distribution results can be compared, and an optimal design scheme from the soundscape perspective can be obtained. Combined with the model output, this study compares the model simulation results after adjusting the design elements and the original, and proposes targeted optimisation strategies for urban park soundscape.