1,720,972 research outputs found
Analysis of the quality of remote working experience: a speech-based approach
Full text linkThe current pandemic situation has led to an extraordinary increase in remote working activities all over the world. In this paper, we conducted a research study with the aim to investigate the Quality of Remote Working Experience (QRWE) of workers when conducting remote working activities and to analyse its correlation with implicit emotion responses estimated from the speech of video-calls or discussions with people in the same room. We implemented a system that captures the audio when the worker is talking and extracts and stores several speech features. A subjective assessment has been conducted, using this tool, which involved 12 people that were asked to provide feedback on the QRWE and assess their sentiment polarity during their daily remote working hours. ANOVA results suggest that speech features may be potentially observed to infer the QRWE and the sentiment polarity of the speaker. Indeed, we have also found that the perceived QRWE and polarity are strongly related
Quality of Experience in the Metaverse: An Initial Analysis on Quality Dimensions and Assessment
Full text linkThe Metaverse provides a novel experience to the user, by opening the doors to social-based multiuser environments merging physical reality with digital virtuality. In this paper, we present an initial analysis of the Quality of Experience (QoE) in the Metaverse. We first consider traditional influence factors (human, system, and context). Then, we introduce the social and economic dimensions of the Metaverse as additional factors to
be considered for QoE assessment. Finally, we discuss what QoE assessment methods can be more suitable for Metaverse applications, with a particular focus on implicit assessment methods (e.g., physiological, human cognitive, affective behaviour)
Controlling Media Player with Hands: A Transformer Approach and a Quality of Experience Assessment
Full text linkIn this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following
classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction
Implementation of a Magnetometer based Vehicle Detection System for Smart Parking applications
No full textThe time lost looking for a free parking spot in a city impacts negatively not only on the mood of the drivers but also on the environment in terms of air quality and fuel consumption. The vehicle detection can be considered as the most important task in Smart Parking systems as it allows to automatically monitor the occupancy state of the parking spots in a city. In this paper, we implement and test a vehicle detection system based on a magnetometer sensor, which is part of a complete Smart Parking system under development at the University of Cagliari. After a preliminary analysis conducted to test the performance of the magnetometer, we conducted two specific experiments to investigate the suitability of the magnetometer as the mean to detect the presence of a vehicle in the parking spots. The first experiment, involving 15 different vehicles, has
demonstrated that the magnetometer can be used to reliably detect the presence of a vehicle in a parking spot if it is placed under the front or rear axle of the vehicle. From the second experiment it resulted that, when considering 3 adjacent parking spots and only one magnetometer placed in the central spot, it is not possible to reliably detect the vehicles parked on the adjacent spots. Therefore, one magnetometer for each considered parking spot is needed
Analysis of Application-layer Data to Estimate the QoE of WebRTC-based Audiovisual Conversations
No full textSuccessful deployment of Web-based Real-Time Communication (WebRTC) applications needs appropriate Quality of Experience (QoE)-aware service management to assure acceptability from the user's perspective. To this aim, monitoring of application-level data was found to provide relevant insights to estimate the user's QoE. In this paper, we investigate the relationship between WebRTC session parameters (collected with the webrtc-internals tool) and the users' QoE (in terms of the Mean Opinion Score (MOS)) through in-depth statistical analysis aimed at identifying the most suitable parameters for QoE estimation. In this regard, we based on statistical metrics, Pearson Correlation Coefficient (PCC), and Analysis of Variance (ANOVA). Then, we trained three machine learning regression algorithms (Regression tree, Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP)) using the identified parameters as the input data and the MOS as the output to be predicted. Experimental results show that the statistical analysis based on the PCC identified the optimal set of WebRTC session parameters for estimating the end user's QoE. With this optimal set of features, the MLP achieved the greatest QoE estimation performance in terms of R2 (0.852) and Root Mean Square Error (RMSE) (0.282), outperforming state-of-the-art results
Robust QUIC-Based Signalling for WebRTC in Impaired Networks
No full textWebRTC (Web Real-Time Communication) has significantly impacted real-time communication in online applications, but the need for standardised signalling protocols still poses significant challenges to its full potential. This paper investigates the impact of using the QUIC (Quick UDP Internet Connections) protocol at the transport layer for signalling and information exchange in WebRTC. A new QUIC-based signalling server for WebRTC is proposed to address the challenge of establishing robust connections between peers in poor network conditions by leveraging the benefits of QUIC. To demonstrate its effectiveness, the performance of the proposed QUIC server is compared with that of the WebSocket-based signalling system, implemented by standard WebRTC solutions, across a network impaired by controlled network distortions, i.e., delay and packet loss. The results show that the proposed QUIC server provides a more robust and efficient solution (i.e., connection is always established and the average connection time is largely reduced) for WebRTC signalling in challenging network conditions
An Analysis of the Trade-Off Between Sustainability and Quality of Experience for Video Streaming
No full textThe massive increase in Internet data traffic caused by video streaming applications has quickly raised energy consumption and carbon dioxide emissions (CO2). Thus, sustainable system solutions have started to be studied, which aim to lower energy consumption but, at the same time, may negatively impact the Quality of Experience (QoE) perceived by the users of video streaming services. This paper aims to investigate the possibility of a trade-off between sustainability and the QoE. We have found that limiting the maximum QoE to an acceptable QoE is a possible solution to reduce energy consumption while still satisfying the consumer. Our finding is partly validated by the results of a survey we conducted to investigate the willingness of people to reduce the video streaming quality being aware that energy saving would be achieved. These results show that most consumers accept lower video quality (to a certain extent) if they have the possibility to consume less energy and save money
The Impact of Network Impairments on the QoE of WebRTC applications: A Subjective study
Full text linkWebRTC-based applications allow for real-time communications that are subject to network impairments affecting the end user's Quality of Experience (QoE). In this paper, we conducted subjective tests involving 20 people to investigate the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. A dedicated system was implemented to introduce controlled network impairments (delay, jitter, and packet loss) to impair the communication between the parties. In addition, test participants had to rate the perceived QoE for the audio, the video, and the overall service, as well as the three emotional dimensions, i.e., valence, arousal, and dominance. Extensive results were obtained regarding the impact of the network impairments on the multimedia quality, the emotional dimensions, and the communication feasibility
WebRTC-QoE: A dataset of QoE assessment of subjective scores, network impairments, and facial & speech features
No full textIn the realm of real-time communications, WebRTC-based multimedia applications are increasingly prevalent as these can be smoothly integrated within Web browsing sessions. The browsing experience is then significantly improved with respect to scenarios where browser add-ons and/or plug-ins are used; still, the end user's Quality of Experience (QoE) in WebRTC sessions may be affected by network impairments, such as delays and losses. Due to the variability in user perceptions under different communications scenarios, comprehending and enhancing the resulting service quality is a complex endeavor. To address this, we present a dataset that provides a comprehensive perspective on the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. This dataset was gathered through subjective evaluations involving 20 subjects across 15 different test conditions (TCs). A specialized system was developed to induce controlled network disruptions such as delay, jitter, and packet loss rate, which adversely affected the communication between the parties. This methodology offered an insight into user perceptions under various network impairments. The dataset encompasses a blend of objective and subjective data including ACR (Absolute Category Rating) subjective scores, WebRTC-internals parameters, facial expressions features, and speech features. Consequently, it serves as a substantial contribution to the improvement of WebRTC-based video call systems, offering practical and real-world data that can drive the development of more robust and efficient multimedia communication systems, thereby enhancing the user's experience
QoE Estimation of WebRTC-based Audio-visual Conversations from Facial and Speech Features
Full text linkThe utilization of user’s facial- and speech-related features for the estimation of the Quality of Experience (QoE) of multimedia services is still underinvestigated despite its potential. Currently, only the use of either facial or speech features individually has been proposed, and relevant limited experiments have been performed. To advance in this respect, in this study, we focused on WebRTC-based videoconferencing, where it is often possible to capture both the facial expressions and vocal speech characteristics of the users. First, we performed thorough statistical analysis to identify the most significant facial- and speech-related features for QoE
estimation, which we extracted from the participants’ audio-video data collected during a subjective assessment. Second, we trained individual QoE estimation machine learning-based models on the separated facial and speech datasets. Finally, we employed data fusion techniques to combine the facial and speech datasets
into a single dataset to enhance the QoE estimation performance due to the integrated knowledge provided by the fusion of facial and speech features. The obtained results demonstrate that the data fusion technique based on the Improved Centered Kernel Alignment (ICKA) allows for reaching a mean QoE estimation accuracy of 0.93, whereas the values of 0.78 and 0.86 are reached when using only facial or speech features, respectively
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