@incollection{RosenthalVersuemerSteffens2022, author = {Rosenthal, Fabian and Vers{\"u}mer, Siegbert and Steffens, Jochen}, title = {Audioinhaltsanalyse und Multilevelmodellierung zur Vorhersage der Bewertung von Indoor Soundscapes}, series = {Deutsche Jahrestagung f{\"u}r Akustik (DAGA)}, booktitle = {Deutsche Jahrestagung f{\"u}r Akustik (DAGA)}, publisher = {DAGA}, address = {Stuttgart}, year = {2022}, language = {de} } @misc{VersuemerSteffensRosenthal2023, author = {Vers{\"u}mer, Siegbert and Steffens, Jochen and Rosenthal, Fabian}, title = {Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception - research data}, edition = {V.01.1}, address = {Zenodo}, doi = {10.5281/zenodo.7193937}, year = {2023}, abstract = {The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi-domain approach including sound-related, person-related, and time-varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non-auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject-centered data, we additionally crowdsourced object-centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low-(self-)noise recorder. These recordings were used to compute (psycho-)acoustical indices such as the energetically averaged loudness level, the A-weighted energetically averaged equivalent continuous sound pressure level, and the A-weighted five-percent exceedance level. This complex hierarchical data can be used to investigate time-varying non-auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.}, subject = {soundscape}, 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} } @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} }