ABSTRACT Environmental acoustics, particularly urban soundscape monitoring, has gained increasing consideration since the United Nations established the Sustainable Development Goals in 2015, and to an even greater extent with the rise of privacy concerns following the introduction of global regulations such as GDPR. As a result, privacy‐compliant devices have become essential for soundscape monitoring in urban environments. In this paper, we present the design and implementation of a portable AI‐driven, privacy‐compliant urban sound recording device that locally captures and processes acoustic data on the edge. In more detail, this device operates as a sensor that captures soundscapes and processes them through a pipeline, which employs a pre‐trained open‐source AI model to anonymize human voices, ensuring privacy without compromising the integrity of the acoustic environment. The anonymization process alters human speech in a way that protects identity while maintaining environmental audio quality. The device can function as a standalone sensor or as part of a synchronized network of distributed sensors. Privacy‐focused evaluation of the device's recordings indicates that, while the anonymization process impacts speech intelligibility, it preserves the overall soundscape with a recall rate of 96%. The system was deployed in a real‐world setting with four temporally synchronized sensors. While network synchronization was achieved, a 1 to 2‐s deviation was occasionally observed in the first duty cycle interval, reflecting timing variability inherent to Cron‐based script triggering. This limitation has been identified for future refinement. This work demonstrates the feasibility of deploying privacy‐compliant, edge‐based soundscape sensors in urban environments, contributing to privacy preservation, and enhanced public safety.

With the evolution of 3D tools, there is now plenty of 3D data for digital applications. This includes 3D retrieval, which seeks to access such data across varied representations such as point clouds, meshes, and multi-view images. However, comprehensive analysis of how to efficiently utilize these representations, or modalities, for retrieval has been missing. This paper evaluates different encodings of each modality in uni-modal retrieval and explores optimal combinations for multimodal retrieval, with state-of-the-art methods from the 3D and image retrieval domains. Results indicate, e.g., that the MuseHash method performs best on mean average precision (MAP), while the CMCL method excels in recall.

This tutorial provides an overview of multimedia retrieval in the context of eXtended Reality (XR), including using virtual and augmented/mixed reality as a user interface for multimedia retrieval, as well as multimedia search tasks addressing content needs for the creation of XR experiences.It will discuss the opportunities and limitations of XR-based search, the evaluation of XR-based multimedia retrieval systems, the demonstration of selected research systems, and open research challenges.

We present the 3D Media Search Engine (3DMSE), which is designed to facilitate the exploration and retrieval of 3D models and images. 3DMSE incorporates unimodal, cross-modal and multimodal retrieval, using any combinations of mesh, point-cloud and multi-image representations. The 3DMSE system is built on the recently proposed MuseHash approach for multimodal representation, and offers a user-friendly web interface that enables formulating queries, presenting search results, and visualising 3D information in an accessible manner.

This paper focuses on the determination of the evoked sentiments to people by observing outdoor and indoor spaces, aiming to create a tool for designers and architects that can be utilized for sophisticated designs. Since sentiment is subjective, the design process can be facilitated by an ancillary automated tool for sentiment extraction. Simultaneously, a dataset containing both real and virtual images of vacant architectural spaces is introduced, while the SUN attributes are also extracted from the images in order to be included throughout training. The dataset is annotated towards both valence and arousal, while five established and two custom architectures, one which has never been used before in classifying abstract concepts, are evaluated on the collected data.

Social media and web sites provide an access to pub-lic opinions on certain aspects and therefore play an important role in getting insights on targeted audiences. Designers have been investigating how to use them for grasping social feelings and needs associated with the arrangement of spaces that surround people in everyday life to find inspiration and come up with ideas for adaptive designs. Following this, we propose a novel design-oriented tool-set that retrieves and analyses online public information from Twitter and focused content from relevant web sites. We present the data collection pipeline and multilingual analysis algorithms like concept extraction and sentiment analysis on interior and urban design. Finally, we showcase an application of the proposed tool-set within two case studies.

Public awareness of and concerns about companies’ social and environmental impacts have seen a marked increase over recent decades. In parallel, the quantity of relevant information has increased, as states pass laws requiring certain forms of reporting, researchers investigate companies’ performance, and companies themselves seek to gain a competitive advantage by being seen to operate fairly and transparently. However, this information is typically dispersed and non-standardized, making it complicated to collect and analyze. To address this challenge, the WikiRate platform aims to collect this information and store it in a standardized format within a centralized public repository, making it much more amenable to analysis. In the context of WikiRate, this article introduces easIE, an easy-to-use information extraction (IE) framework that leverages general Web IE principles for building datasets with environmental, social, and governance information from the Web. To demonstrate the flexibility and value of easIE, we built a large-scale corporate social responsibility database comprising 654,491 metrics related to 49,009 companies spending less than 16 hours for data engineering, collection, and indexing. Finally, a data collection exercise involving 12 subjects was performed to showcase the ease of use of the developed framework.

In this paper an object-based method to generate additional stereo views from monoscopic image sequences using rigid motion and structure estimation by extended Kalman filters is presented. First, rigid scene objects are segmented and feature points in each object are extracted and tracked throughout the video sequence. Then, motion, structure and focal length are estimated recursively for each object, using extended Kalman filters, as described in Azarbayejani and Pentland (1995). Furthermore, the feature points and depths in the stereo image are computed and interpolated using 2-D Delaunay triangulation. Finally, a stereo image generation algorithm is proposed that uses the camera and structure equations to project the 3-D points in a new virtual stereo view for each frame. The generation of stereoscopic scenes is possible even when multiple moving rigid objects exist in the scene. Experimental results show that a layered stereo object representation yields improved results.