Akteure
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Akteursliste
- Forschung
The Binaural Rendering Toolbox. A Virtual Laboratory for Reproducible Research in Psychoacoustics
The Binaural Rendering Toolbox (BRT) is a set of software libraries, applications, and definitions aimed as a virtual laboratory for psychoacoustic experimentation.The BRT is developed in the framework of the SONICOM project 1 and will include the algorithms developed in the 3D Tune-In Toolkit 2 in a new open, extensible architecture.At the core of the BRT Toolbox, a library provides C++ implementations of listener models, source models, and environment models, including a growing collection of portings to different audio frameworks such as PureData, MaxMSP and VST plugins, by means of the Avendish library.In addition, the BRT also includes an application controlled via the Open Sound Control (OSC) protocol.This paper describes the architecture of the BRT, its main features, and its application to reproducible psychoacoustics experiments.The toolbox provides a complete trace of the experiment, including the delivered binaural audio, annotated with the listener and source movements.For this purpose, a new SOFA convention is proposed to store dynamic measurements, facilitating their use in the Auditory Model Toolbox (AMT).
Medien & AV - Forschung
Digital Technologies and Material Passports for Circularity in Buildings: An In-Depth Analysis of Current Practices and Emerging Trends
Abstract The construction industry is undergoing a significant transformation driven by digitalization and an unwavering commitment to implementing circular economy (CE) principles and sustainability into its core practices. Emerging digital technologies (DTs), such as Material Passports (MPs), Building Information Modelling (BIM) Artificial Intelligence (AI) and Scanning technologies, Blockchain technology (BCT), the Internet of Things (IoT) stand out as pivotal tools capable of expediting the transition towards CE implementation in buildings. This study highlights the significant potential of six DTs to support CE application throughout the building lifecycle. Furthermore, it delves into the potential synergies among these diverse DTs, highlighting the additional benefits that collaboration can bring across different lifecycle stages of a building project. Particular emphasis is placed on the integration of MPs with other DTs, showing promise in assessing resource availability, volumes, and flows. This integration optimizes waste reduction and recycling plans, contributing to more precise selective and smart deconstruction planning. The combined use of DTs offers substantial benefits to stakeholders, enabling them to make informed decisions regarding maintenance and understand the current quality of specific materials. Through these means, the study aims to provide a comprehensive overview of the array of DTs propelling circular building practices. It also explores emerging trends in this dynamic field, scrutinizing the effectiveness of adopting these technologies throughout the building life cycle stages, and anticipating potential challenges these technologies may face.
Architektur - Forschung
Automation of escape route analysis for BIM-based building code checking
Forschungsarbeit zur halbautomatischen Fluchtweganalyse für die BIM-basierte Bauordnungsprüfung, validiert an realen Gebäudemodellen in Solibri Office.
Architektur - Forschung
Using deep learning to generate design spaces for architecture
We present an early prototype of a design system that uses Deep Learning methodology—Conditional Variational Autoencoders (CVAE)—to arrive at custom design spaces that can be interactively explored using semantic labels. Our work is closely tied to principles of parametric design. We use parametric models to create the dataset needed to train the neural network, thus tackling the problem of lacking 3D datasets needed for deep learning. We propose that the CVAE functions as a parametric tool in itself: The solution space is larger and more diverse than the combined solution spaces of all parametric models used for training. We showcase multiple methods on how this solution space can be navigated and explored, supporting explorations such as object morphing, object addition, and rudimentary 3D style transfer. As a test case, we implemented some examples of the geometric taxonomy of “Operative Design” by Di Mari and Yoo.
Architektur - Forschung
Deep learning’s shallow gains: a comparative evaluation of algorithms for automatic music generation
Abstract Deep learning methods are recognised as state-of-the-art for many applications of machine learning. Recently, deep learning methods have emerged as a solution to the task of automatic music generation (AMG) using symbolic tokens in a target style, but their superiority over non-deep learning methods has not been demonstrated. Here, we conduct a listening study to comparatively evaluate several music generation systems along six musical dimensions: stylistic success, aesthetic pleasure, repetition or self-reference, melody, harmony, and rhythm. A range of models, both deep learning algorithms and other methods, are used to generate 30-s excerpts in the style of Classical string quartets and classical piano improvisations. Fifty participants with relatively high musical knowledge rate unlabelled samples of computer-generated and human-composed excerpts for the six musical dimensions. We use non-parametric Bayesian hypothesis testing to interpret the results, allowing the possibility of finding meaningful non -differences between systems’ performance. We find that the strongest deep learning method, a reimplemented version of Music Transformer, has equivalent performance to a non-deep learning method, MAIA Markov, demonstrating that to date, deep learning does not outperform other methods for AMG. We also find there still remains a significant gap between any algorithmic method and human-composed excerpts.
Medien & AV - Forschung
AI Diffusion as Design Vocabulary - Investigating the use of AI image generation in early architectural design and education
This paper investigates the potential of Text-to-Image AI in assisting the ideation phase in architectural design and education. The study proposes a structured workflow and tests it with first-year architecture students. It aims to create a comprehensive design vocabulary by using AI-generated images as primary design references and incorporating them into a modelling workflow. The paper implements a process combining specific vocabulary extraction, image generation, 2D to 3D translation, and spatial composition within a six weeklong design course. The findings suggest that such a process can enhance the ideation phase by generating new and diverse design inspirations, improve spatial understanding through the exploration of various design elements, and provide students with a targeted visual vocabulary that helps define design intention and streamlines the modelling process.
Architektur