@phdthesis{Versuemer2025, author = {Vers{\"u}mer, Siegbert}, title = {Generalized prediction of quiet indoor soundscapes based on retrospective and in-situ judgements}, publisher = {Berlin}, address = {Technische Universit{\"a}t Berlin}, doi = {10.14279/depositonce-23962}, pages = {199}, year = {2025}, abstract = {This dissertation aims to enhance the understanding of how people perceive and react to everyday sounds, particularly in their homes. The focus is laid on indoor soundscapes and the interplay between acoustic measures, individual differences, and contextual factors. A retrospective online study and a field study at peoples' homes based on the Experience Sampling Method were the ecologically valid ground truth for four key publications. These address significant gaps in soundscape research by providing insights into the factors influencing sound perception in real-life contexts, the evaluation of low-level (i.e., quiet) sounds, and the consequences of (in)appropriate statistical analysis of imbalanced hierarchical soundscape data. The studies reveal that the sound source category is a critical predictor of annoyance and the pleasantness of soundscapes, with different types of sounds (e.g., natural, human, technical) having distinct impacts. Contrary, the effect of acoustic measures like perceived and calculated loudness on sound perception was expected to be high but was masked by contextual factors. These were the perceived control over the acoustic situation and the affective state of a person in the specific situation, proving context-related perceptual measures to be more important than the sound itself. The research consequently highlights the need for automatic sound source identification in complex polyphony everyday sound environments and suggests that individual preferences and liking of sounds could be potential factors in predicting individual sound perception. The dissertation also emphasizes the importance of using appropriate statistical methods, including mixed-effects models and nonlinear regression techniques, to generate more generalizable and more valid results based on hierarchical and imbalanced soundscape data. The findings underscore the limitations of traditional acoustic metrics and advocate for the use of time-series data to better capture the dynamic nature of everyday sound environments in contrast to stimuli typically used in laboratory studies. The author further developed a multi-objective function for avoiding both over- and underfitting during hyperparameter tuning, significantly improving generalized model fitting in soundscape research. Overall, this work contributes to the field of soundscape research by providing a comprehensive analysis of sound perception in indoor environments, highlighting the importance of context, individual differences, and advanced statistical methods in understanding and modeling soundscapes. Finally, future perspectives were discussed, such as the focus on individual preferences, experiences, and expectations for the improved prediction of individual sound perception.}, subject = {Psychoakustik}, language = {en} }