@article{VersuemerBlaettermannRosenthaletal.2025, author = {Vers{\"u}mer, Siegbert and Bl{\"a}ttermann, Patrick and Rosenthal, Fabian and Weinzierl, Stefan}, title = {A comparison of methods for modeling soundscape dimensions based on different datasets}, series = {The Journal of the Acoustical Society of America}, volume = {157}, journal = {The Journal of the Acoustical Society of America}, number = {1}, publisher = {AIP Publishing}, issn = {1520-8524}, doi = {10.1121/10.0034849}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-48849}, pages = {234 -- 255}, year = {2025}, abstract = {Soundscape studies vary considerably in study design, statistical methods, and model fit metrics used. Due to this confounding of data and methods, it is difficult to assess the suitability of statistical modelling techniques used in the literature. Therefore, five different methods and two performance metrics were applied to three existing soundscape datasets to model soundscape Pleasantness and Eventfulness based on seven acoustic and three sociodemographic predictors. Datasets differed in soundscape type (urban outdoor vs indoor), experimental setting (field- vs lab-based), size, and study design (site- vs person-centered). The fixed-effects and mixed-effects methods ranged from linear to nonlinear regression based on advanced machine learning approaches. Results showed that models performed better for Eventfulness than for Pleasantness in most cases, while performance as measured by the out-of-sample R2 was dependent on the total variance of the target, especially in both field studies with imbalanced targets and groups. Nonlinear methods consistently outperformed linear regression, with random forest and extreme gradient boosting performing particularly well, while the performance levels of all nonlinear methods remained comparable. Mixed-effects models provided a more generalized, albeit slightly smaller prediction performance when tested on unknown groups. Finally, this study motivates the use of cross-validation with special splitting for analyzing small imbalanced datasets.}, subject = {Akustische Eigenschaft}, language = {en} } @inproceedings{VersuemerBlaettermann2024, author = {Vers{\"u}mer, Siegbert and Bl{\"a}ttermann, Patrick}, title = {Predicting real indoor soundscapes based on auditory and non-auditory factors across different loudness ranges with linear and nonlinear models}, series = {INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Conference Proceeding 8, 4 October 2024}, booktitle = {INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Conference Proceeding 8, 4 October 2024}, publisher = {Institute of Noise Control Engineering}, doi = {10.3397/IN_2024_3386}, pages = {3882 -- 3892}, year = {2024}, subject = {Soundscape}, language = {en} } @inproceedings{BlaettermannVersuemerSteffens2025, author = {Bl{\"a}ttermann, Patrick and Vers{\"u}mer, Siegbert and Steffens, Jochen}, title = {Loadings of Acoustical Metrics on Soundscape Items and their relation to Soundscape Dimensions across different datasets [Abstract]}, series = {DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen}, booktitle = {DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen}, publisher = {Deutsche Gesellschaft f{\"u}r Akustik e.V.}, address = {Berlin}, year = {2025}, subject = {Soundscape}, language = {en} } @article{vonBergVersuemerBittaetal.2025, author = {von Berg, Markus and Vers{\"u}mer, Siegbert and Bitta, Joshua and Steffens, Jochen}, title = {Reduced reproduction levels of outdoor soundscapes are deemed appropriate - even after real-world exposure}, series = {Acta Acustica}, journal = {Acta Acustica}, publisher = {EDP Sciences}, issn = {2681-4617}, doi = {10.1051/aacus/2025062}, pages = {16}, year = {2025}, abstract = {Laboratory experiments in psychoacoustical and soundscape research indicate that participants perceive a reproduction sound level lowered by 8-10 dB as more plausible than the original level. This bias supposedly roots in an adaptation of perceptual loudness scaling to the laboratory environment, that is overall quieter than urban outdoor soundscapes. To gain further insights into the nature of such loudness bias, we conducted a listening experiment in both field and laboratory using a within-subjects design. Thirty-one participants visited a street and listened to the environmental sounds for one minute, while these sounds were also recorded using a dummy head. Thereafter, they listened to the recording in a quiet laboratory nearby and adjusted its level as they remembered it. About half of the sample did this immediately, the other half about 20 minutes after the recording. Results confirm a bias towards lower levels with a mean of about 8.9 dB, regardless of the time between the recording and the reproduction in the laboratory. Also, participants with higher musical abilities tended to select higher, more accurate levels, whereas noise-sensitive participants deemed lower levels appropriate. Results suggest that the hypothesized adaptation of perceptual scaling to the laboratory happens immediately and is affected by individual factors.}, subject = {Psychoakustik}, language = {en} } @misc{vonBergVersuemerSteffensetal.2025, author = {von Berg, Markus and Vers{\"u}mer, Siegbert and Steffens, Jochen and Bitta, Joshua}, title = {Reproduction levels of outdoor soundscapes in the lab after in-situ exposure: participant responses and recording level envelopes}, publisher = {Zenodo}, doi = {10.5281/zenodo.17663267}, year = {2025}, abstract = {Supplemental material for https://doi.org/10.1051/aacus/2025062 To investigate loudness biases in the lab, where outdoor sounds are perceived as being too loud if reproduced with physically accurate levels in the lab, a listening experiment was conducted, where thirty-one participants visited a street and listened to the environmental sounds for one minute. These sounds were also recorded using a dummy head. Afterwards, participants listened to the recording in a quiet laboratory and adjusted its level as they remembered it. About half of the sample did this immediately, the other half about 20 min after the recording, during which the performed another listening tests. Afterwards, previous experience with acoustics, musical sophistication and noise sensitivity were assessed. The results revealed a mean gain adjustment of 8.9 dB below the original. The data uploaded here comprises participants gain adjustment, the recording levels averaged other A) the entire recording and B) the first and last 7.5s or 15s, respecitvely and all questionnaire responses as well as aggregate scores for musical sophistication and noise sensitivity, as well as a binary categorization of the respondent into laymen and experienced, based on their previous experience with acoustics. For sociodemographic information, participants age, sex and educational degree are reported as well. Content - 00_Description: - Description of the data files - Includes a list of explanation of the columns names in "data_questionnaires_aggregateLevels.csv - data_questionnaires_aggregateLevels.csv - Participants' adjusted recordings levels - Several level and loudness aggregates of each participant's recording that were analyzed in the paper - Participants' responses to the administered questionnaires - level_envelopes.zip - Archive with 31 CSV files containing level envelopes of each participant's recording. - A-weighted and unweighted level envelope for the dummy head's left and right ear - One-minute recordings with a temporal resolution of 125ms}, subject = {Psychoakustik}, language = {en} } @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} }