In this work, an emotion recognition system for enhancing social XR applications is presented. Although several techniques for emotion recognition have been proposed in the literature, they either require invasive and advanced equipment or exploit facial expressions, speech excerpts, physiological data, and text. In this contribution, on the contrary, an approach for markerless emotion classification through body language is designed. More specifically, human movements are analyzed over time by extracting the skeleton joints in videos acquired by consumer cameras. A normalization procedure has been introduced to provide a depth-independent skeleton representation without distorting the skeleton shape. The performance of the proposed method have been assessed using a dataset of videos recorded from multiple points of view. An ad-hoc learning-based emotion classifier has been trained to recognize four emotions (happiness, boredom, interest, and disgust) achieving an average accuracy of 72.5%. The pre-processed dataset, code, and demo with pre-trained models are available at https://github.com/michaelneri/emotion-recognition-human-movements . • We define a human skeleton representation that does not change with the distance between the user and the camera. • We introduce a new approach for classifying emotions based exclusively on body movements. • We extend an existing dataset employing multiple points of view for classifying four emotional states: happiness, interest, boredom, and disgust.

Distance estimation from audio plays a crucial role in various applications, such as acoustic scene analysis, sound source localization, and room modeling. Most studies predominantly center on employing a classification approach, where distances are discretized into distinct categories, enabling smoother model training and achieving higher accuracy but imposing restrictions on the precision of the obtained sound source position. Towards this direction, in this paper we propose a novel approach for continuous distance estimation from audio signals using a convolutional recurrent neural network with an attention module. The attention mechanism enables the model to focus on relevant temporal and spectral features, enhancing its ability to capture fine-grained distance-related information. To evaluate the effectiveness of our proposed method, we conduct extensive experiments using audio recordings in controlled environments with three levels of realism (synthetic room impulse response, measured response with convolved speech, and real recordings) on four datasets (our synthetic dataset, QMULTIMIT, VoiceHome-2, and STARSS23). Experimental results show that the model achieves an absolute error of 0.11 meters in a noiseless synthetic scenario. Moreover, the results showed an absolute error of about 1.30 meters in the hybrid scenario. The algorithm's performance in the real scenario, where unpredictable environmental factors and noise are prevalent, yields an absolute error of approximately 0.50 meters. For reproducible research purposes we make model, code, and synthetic datasets available at https://github.com/michaelneri/audio-distance-estimation

Artificial Intelligence techniques are being applied in the quality assessment of immersive multimedia content, such as virtual and augmented reality scenarios. The immersive nature of these applications poses a unique challenge to traditional quality assessment methods. In fact, estimating user acceptance of immersive technologies is complex due to multiple aspects, such as usability, enjoyment, and cyber sickness. Artificial Intelligence-based approaches offer a promising solution to this problem, enabling objective evaluations of immersive multimedia such as spatial audios, point clouds, and light field images. This work presents an overview of different artificial intelligence techniques that have been used for quality assessments of immersive multimedia content, including machine learning algorithms, deep learning, and computer vision. The advantages of these techniques and some examples of practical application are provided. Future works are presented, underlining the possible outcomes of a Ph.D. study in this field.