@article{VersuemerSteffensBlaettermannetal.2020, author = {Vers{\"u}mer, Siegbert and Steffens, Jochen and Bl{\"a}ttermann, Patrick and Becker-Schweitzer, J{\"o}rg}, title = {Modelling evaluations of low-level sounds in everyday situations using linear machine learning for variable selection}, series = {Frontiers in Psychology}, volume = {11}, journal = {Frontiers in Psychology}, publisher = {Frontiers}, issn = {1664-1078}, doi = {10.3389/fpsyg.2020.570761}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-23779}, year = {2020}, language = {en} } @unpublished{PtasczynskiBlaettermannGrebetal.2023, author = {Ptasczynski, Lena Esther and Bl{\"a}ttermann, Patrick and Greb, Fabian and Sterzer, Philipp and Steffens, Jochen}, title = {The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic}, series = {Research Square}, journal = {Research Square}, edition = {v1}, publisher = {Research Square}, doi = {10.21203/rs.3.rs-2529116/v1}, pages = {34}, year = {2023}, abstract = {Music listening is subjectively efficient in surrogating socioemotional needs during the Covid-19 pandemic, although underlying mechanisms remain unclear. We here challenge the generalizability of music-based coping by testing whether the insufficient satisfaction of distinct needs during the first lockdown entails enhanced listening to beat-based music with medium complexity (BBMMC) stimulating the cortico-striatal reward circuit and triggering oxytocinergic propagation. A Quasi-Bayesian mediation analysis on open-source data revealed that, across 11 European countries, more severe social distancing measures evoke a decline in experienced social provision (SP), in turn provoking BBMMC preferences. The latter was best predicted by the SP dimensions of social reward and attachment, suggesting that the partial overlap in neurophysiological processing at dopaminergic and oxytocinergic sites enables BBMMC to specifically alleviate these socioemotional needs, which has important implications for the development of mental health interventions. Finally, a stress-alleviating effect of BBMMC was found pointing towards HPA-activity modulation through music listening.}, subject = {Psychoakustik}, language = {en} } @misc{PtasczynskiBlaettermannGregetal.2023, author = {Ptasczynski, Lena Esther and Bl{\"a}ttermann, Patrick and Greg, Fabian and Sterzer, Philipp and Steffens, Jochen}, title = {Dataset related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic"}, edition = {v0.9}, publisher = {Zenodo}, doi = {10.5281/zenodo.7669360}, year = {2023}, abstract = {The set includes data related to the paper "The vibe of musical and social reward: Listening to beat-based music acts as a surrogate for socioemotional support during the Covid-19 pandemic" *************** Information *************** Dataset of the related text publication: https://doi.org/10.21203/rs.3.rs-2529116/v1}, language = {en} } @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} } @misc{RosenthalVersuemerBlaettermann2024, author = {Rosenthal, Fabian and Vers{\"u}mer, Siegbert and Bl{\"a}ttermann, Patrick}, title = {flexcv: Easy and flexible nested cross validation for tabular data in python}, edition = {Version v24.0-beta}, publisher = {Zenodo}, address = {Genf}, doi = {10.5281/zenodo.10160846}, year = {2024}, abstract = {Python package customizing nested cross validation for tabular data.}, subject = {Software}, language = {en} } @misc{SteffensBlaettermannSattleretal.2025, author = {Steffens, Jochen and Bl{\"a}ttermann, Patrick and Sattler, Maximilian and Tang, Kevin}, title = {How does a credible voice sound? [Project Repository]}, publisher = {Open Science Framework}, doi = {10.17605/OSF.IO/MTCUH}, year = {2025}, abstract = {A project repository for the paper: Steffens, Bl{\"a}ttermann, Sattler and Tang (2025, "How does a credible voice sound?"). It contains three components: the dataset, data analyses and the materials to elicit the speech data.}, subject = {Sprechen}, language = {en} } @inproceedings{RosenthalBlaettermannVersuemer2024, author = {Rosenthal, Fabian and Bl{\"a}ttermann, Patrick and Vers{\"u}mer, Siegbert}, title = {flexcv: Python package for fitting, comparing, and logging multiple machine learning models using various cross-validation methods [Poster]}, series = {Fortschritte der Akustik - DAGA 2024, 18. - 21. M{\"a}rz 2024, Hannover}, booktitle = {Fortschritte der Akustik - DAGA 2024, 18. - 21. M{\"a}rz 2024, Hannover}, publisher = {Deutsche Gesellschaft f{\"u}r Akustik e.V.}, address = {Berlin}, year = {2024}, abstract = {The evaluation of listening experiments and studies on acoustics not only requires basic knowledge of statistics, but also poses challenges in the implementation of different methodologies. Especially for small sample sizes and peculiarities regarding hierarchical data structures, the need to individualize evaluation scripts arises. Therefore, we introduce flexcv, a powerful machine learning package for Python for evaluating various models on experimental tabular data, especially with small sample sizes. It supports random effects evaluation (including random slopes) for both linear and non-linear regressors, providing broad applicability to different experiments and research questions.flexcv quickly allows to perform nested cross-validation on a variety of models for comparison with each other. On the one hand, the implementation of a flexible interface simplifies the exchange of methods in the script, allowing researchers to change cross-validation methods without having to touch the actual cross-validation code. On the other hand, extensive online logging allows and simplifies the evaluation and experiment tracking along the process and different machines.}, 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{VersuemerBlaettermannSteffens2025, author = {Vers{\"u}mer, Siegbert and Bl{\"a}ttermann, Patrick and Steffens, Jochen}, title = {Nonlinearities in generalized models based on different soundscape 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} } @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{SteffensBlaettermannSattleretal.2025, author = {Steffens, Jochen and Bl{\"a}ttermann, Patrick and Sattler, Maximilian and Tang, Kevin}, title = {How does a credible voice sound?}, series = {The Journal of the Acoustical Society of America}, volume = {157}, journal = {The Journal of the Acoustical Society of America}, number = {5}, publisher = {Acoustical Society of America}, issn = {1520-8524}, doi = {10.1121/10.0036646}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-53133}, pages = {3780 -- 3792}, year = {2025}, abstract = {Credibility is a crucial social factor that influences people's perception and decision-making. This study explores the acoustic attributes that define credible speech compared to neutral and ironic speech. A custom-built German corpus was developed, containing speech samples recorded from amateurs to enhance ecological validity. The study extracted a broad set of audio features from these samples, employing recursive feature elimination to identify the most influential attributes. These were then analyzed using a machine-learning-supported multinomial logistic regression model. The results indicated significant differences in the acoustic features associated with credible speech compared to neutral and ironic speech. Key findings include the role of a higher energy level (1st mel-frequency cepstral coefficient) in credible compared to neutral and ironic speech and a higher speaking rate in both credible and ironic compared to neutral speech. Also, irony is characterized by more high-frequency content (mean spectral centroid) compared to credible or neutral speech. Gender differences in spoken irony involve a greater influence of speaking rate in women's speech, while high pitch plays a more significant role in men's speech. This research thus contributes to the understanding of how credibility is conveyed through speech and offers insights for applications in communication, media, and artificial intelligence. The study also highlights the methodological advancements made by incorporating a diverse range of acoustic features and employing a robust machine-learning framework.}, subject = {Sprechen}, language = {en} }