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An interactive virtual studio is used for live broadcasts with live interaction in front of the recording system. Advances in Augmented Reality (AR) using video see-through Head-Mounted Displays (HMDs) also make it possible to improve the production process itself. Lighting can be improved and adjusted using AR while seeing live effects. An Augmented Reality Lighting Adjustment System (ARLAS) for a virtual studio has been developed and evaluated. The traditional process for controlling lighting in a virtual studio is complicated and time-consuming. It often requires several people to control many different components at different locations. Using AR, this process is simplified by integrating relevant programs and video streams into a single application on an HMD, allowing direct control at lighting locations. Changes to settings are immediately visible on the real fixtures. Real lights can be replicated virtually to maintain consistent lighting conditions in virtual scenes, ensuring that virtual objects are lit as they would be in a real studio. The prototype demonstrated the benefits of AR for complex virtual studio setups and was evaluated by 18 participants.
Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein- Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) sowie §§ 2 Abs. 4, 56 Abs. 1 des Gesetzes über die Kunsthochschulen des Landes Nordrhein-Westfalen (Kunsthochschulgesetz - KunstHG -) vom 13.03.2008 (GV. NRW. S. 195) in den jeweils aktuell gültigen Fassungen haben die Hochschule Düsseldorf und die Robert Schumann Hochschule Düsseldorf einvernehmlich die folgende Prüfungsordnung für den gemeinsamen Bachelorstudiengang Ton und Bild als Satzung erlassen.
Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Prüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit der Rahmenprüfungsordnung des Fachbereichs Medien (RahmenPO) an der Hochschule Düsseldorf vom 27.08.2025 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 1019).
Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Prüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit der Rahmenprüfungsordnung des Fachbereichs Medien (RahmenPO) an der Hochschule Düsseldorf vom 27.08.2025 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 1019).
Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Prüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit der Rahmenprüfungsordnung des Fachbereichs Medien (RahmenPO) an der Hochschule Düsseldorf vom 27.08.2025 (Verkündungsblatt der Hochschule Düsseldorf, Amtliche Mitteilung Nr. 1019).
Aufgrund der §§ 2 Abs. 4, 64 Abs. 1 des Gesetzes über die Hochschulen des Landes Nordrhein-Westfalen (Hochschulgesetz - HG) vom 16.09.2014 (GV. NRW. S. 547) in der aktuell gültigen Fassung hat die Hochschule Düsseldorf die folgende Rahmenprüfungsordnung als Satzung erlassen. Diese Ordnung gilt nur in Verbindung mit den studiengangsspezifischen Bestimmungen der einzelnen Studien-gänge im Fachbereich Medien.
Generalized prediction of quiet indoor soundscapes based on retrospective and in-situ judgements
(2025)
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.
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym -- the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at this https URL [https://github.com/pluskal-lab/MassSpecGym].