TY - CHAP A1 - Versümer, Siegbert A1 - Steffens, Jochen A1 - Blättermann, Patrick T1 - Subjektive Lautheitsbewertung unter Einfluss situativer und personenbezogener Faktoren T2 - Deutsche Jahrestagung für Akustik (DAGA) Y1 - 2022 CY - Stuttgart ER - TY - CHAP A1 - Rosenthal, Fabian A1 - Blättermann, Patrick A1 - Versümer, Siegbert T1 - flexcv: Python package for fitting, comparing, and logging multiple machine learning models using various cross-validation methods [Poster] T2 - Fortschritte der Akustik - DAGA 2024, 18. - 21. März 2024, Hannover N2 - 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. Y1 - 2024 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - CHAP A1 - Steffens, Jochen A1 - Blättermann, Patrick A1 - Joschko, Marcel T1 - Exploring Album Dramaturgy: A Computational Music Analysis Across Pop Music History [Abstract] T2 - escom12 - The 12th Triennial Conference of the European Society for the Cognitive Sciences of Music: Book of Abstracts N2 - Background A musical album can be considered a central artwork of an artist, and the order of its songs can influence the way the album is perceived by the listener. That is, purposeful sequencing can create an emotional journey and suspense (Lehne & Kölsch, 2015), tell a story or reinforce a particular concept or theme in the artist's music. Aim(s) This study thus aimed on examining the evolution of albums over the course of pop music history and on unveiling patterns (i.e. clusters) and temporal shifts in emotion- related album dramaturgy dependent on the cultural context (in terms of genres) by utilizing advanced computational methods. Methods We first created a dataset by scraping titles and artists of the weekly Top 10 albums (n = 14.394) from the Billboard.com charts from 1957 to 2022. We further extracted the emotion-related audio descriptors ‘valence’ and ‘energy’ from the Spotify Developer API for each track of the corresponding album, resulting in two-dimensional time series. We then employed Deep Time Series Embedding Clustering (DeTSEC; lenco et al., 2020) which can handle multivariate time series of different length. Finally, k-means clustering was applied on the embeddings produced by the DeTSEC, resulting in a 12 cluster solution. Results To facilitate the interpretation of our solution, we determined the most representative album of a cluster using the smallest Euclidean distance to its centroid. Preliminary time series analysis of the resulting 12 albums confirm different emotional trajectories of the respective clusters, for instance showing decreasing valence and arousal values over time for “I am up” by Young Thug (Cluster 6) or highest valence and arousal values at the beginning and the end of the album for“The New Classic” by Iggy Azalea (Cluster 12). Furthermore, Chi² tests revealed significant associations between an album’s cluster membership and both its year of release and its genre. Discussion and Conclusion The study constitutes a first step in shedding light on the evolution of albums in modern popular music since its emergence. It further provides an insight into how temporal dramaturgies in terms of the emotion-related arrangement of songs of albums differ across genres and undergo cultural trends and temporal shifts. References Ienco D, & Interdonato R (2020). Deep Multivariate Time Series Embedding Clustering via Attentive-Gated Autoencoder. Advances in Knowledge Discovery and Data Mining. 318–329 Lehne, M., & Koelsch, S. (2015). Toward a general psychological model of tension and suspense. Frontiers in Psychology, 6, 79. KW - Popmusik KW - Dramaturgie Y1 - 2024 U6 - https://doi.org/10.25364/602.2024.3 SP - 245 EP - 246 PB - Universität Graz CY - Graz ER - TY - CHAP A1 - Versümer, Siegbert A1 - Blättermann, Patrick T1 - Predicting real indoor soundscapes based on auditory and non-auditory factors across different loudness ranges with linear and nonlinear models T2 - INTER-NOISE and NOISE-CON Congress and Conference Proceedings, Conference Proceeding 8, 4 October 2024 KW - Soundscape KW - Lautstärke KW - Psychoakustik Y1 - 2024 U6 - https://doi.org/10.3397/IN_2024_3386 SP - 3882 EP - 3892 PB - Institute of Noise Control Engineering ER - TY - CHAP A1 - Versümer, Siegbert A1 - Blättermann, Patrick A1 - Steffens, Jochen T1 - Nonlinearities in generalized models based on different soundscape datasets [Abstract] T2 - DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen KW - Soundscape KW - Datenanalyse KW - Regressionsanalyse KW - Lineares Regressionsmodell KW - Nichtlineares Regressionsmodell KW - Verallgemeinertes Regressionsmodell Y1 - 2025 UR - https://pub.dega-akustik.de/DAS-DAGA_2025/konferenz-2008.html?article=433 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - CHAP A1 - Blättermann, Patrick A1 - Versümer, Siegbert A1 - Steffens, Jochen T1 - Loadings of Acoustical Metrics on Soundscape Items and their relation to Soundscape Dimensions across different datasets [Abstract] T2 - DAS|DAGA 2025: 51st Annual Meeting on Acoustics, March 17-20, 2025, Copenhagen KW - Soundscape KW - Psychoakustik KW - Datenanalyse KW - Maschinelles Lernen Y1 - 2025 UR - https://pub.dega-akustik.de/DAS-DAGA_2025/konferenz-2007.html?article=383 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER -