This paper delves into addressing road safety concerns by exploring cost-effective solutions for detecting sound events (SED) specifically designed for driving scenarios. While advanced technologies such as deep learning show promise in enhancing road safety, their practical implementation often involves expensive sensors and hardware. Given that distractions are a significant contributor to accidents, it is crucial to have effective detection and mitigation measures in place. This study focuses on auditory distractions and employs SED with affordable edge devices to identify and timestamp relevant audio events, offering valuable insights into the driving environment. We assess the performance of state-of-the-art deep learning models on various edge devices, including the 2023 DCASE baseline with convolutional recurrent neural networks (CRNN) and a customized YOLO vision model for audio spectrograms. Our analysis spans different hardware options, ranging from single board computers (SBCs) to desktop equipment, providing guidance on selecting cost-effective hardware for in-vehicle SED applications. The research aims to contribute to the development of affordable SED solutions in the context of driving safety, with the ultimate goal of advancing road safety initiatives globally.

Traditional tools used to evaluate the Quality of Experience (QoE) of users after browsing an ad, using a product, or performing any kind of task typically involves surveys, user testing, and analytics. However, these methods provide limited insights and have limitations due to the need of users’ active cooperation and sincerity, the long testing time, the high cost, and the limited scalability. On this work we present the tools we are developing to automatically evaluate QoE in different use cases such as dashboards that show on real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data. To develop these tools, we require datasets on affective computing. We highlight some limitations of the available ones, the difficulties during the creation of such data, and our current work in the confection of a new one with automatic annotation of ground truth.

This paper contains the research proposal of Juan Antonio De Rus presented at the IMX 23 Doctoral Symposium. Virtual Reality (VR) applications are already used to support diverse tasks such as online meetings, education, or training, and the usages grow every year. To enrich the experience VR scenarios, include multimodal content (video, audio, text, synthetic content) and multi-sensory stimuli are typically included. Tools to evaluate the Quality of Experience (QoE) of such scenarios are needed. Traditional tools used to evaluate the QoE of users performing any kind of task typically involves surveys, user testing or analytics. However, these methods provide limited insights for our tasks with VR and have shortcomings and a limited scalability. In this doctoral study we have formulated a set of open research questions and objectives on which we plan to generate contributions and knowledge in the field of Affective Computing (AC) and Multimodal Interactive Virtual Environments. Hence, in this paper we present a set of tools we are developing to automatically evaluate QoE in different use cases. They include dashboards to monitor in real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data, as well as the creation of a dataset for AC and its associated methodology.

When designing and deploying multimedia systems, it is essential to accurately know about the necessary requirements and the Quality of Service (QoS) offered to the customers. This paper presents two open-source software tools that contribute to these key needs. The first tool is able to measure and register resources consumption metrics for any Windows program (i.e. process id), like the CPU, GPU and RAM usage. Unlike the Task Manager, which requires manual visual inspection for just a subset of these metrics, the developed tool runs on top of the Powershell to periodically measure these metrics, calculate statistics, and register them in log files. The second tool is able to measure QoS metrics from DASH streaming sessions by running on top of TShark, if a non-secure HTTP connection is used. For each DASH chunk, the tool registers: the round-trip time from request to download, the number of TCP segments and bytes, the effective bandwidth, the selected DASH representation, and the associated parameters in the MPD (e.g., resolution, bitrate). It also registers the MPD and the total amount of downloaded frames and bytes. The advantage of this second tool is that these metrics can be registered regardless of the player used, even from a device connected to the same network than the DASH player.

This paper reports on the work being done towards achieving an immersive and accessible reconstruction of Historical and Cultural Heritage, focusing on Theaters of the Early Modern as use case. In particular, the paper presents and discusses potential possibilities to enable: 1) the acoustical reconstruction of the virtual (lost) environments – beyond the graphical reconstruction for the buildings, elements and performances; 2) effective interaction features and navigation within the virtual environment (e.g. by means of adaptive interfaces, guiding methods, insertion of Point of Interest); and 3) accessible experiences, by means of an innovative and personalized presentation modes for access services, like subtitles and audio description. For most of these aspects and features, proof of concept implementation are provided, and opportunities for future work are outlined. With an effective combination of all these contributions, the goal is to bring back valuable (both tangible and intangible) Cultural Heritage from the past, providing high benefits in relevant sectors like Culture, Tourism and Education.