In immersive Audio Augmented Reality, a virtual sound source should be indistinguishable from the existing real ones. This property can be evaluated with the co-immersion criterion, which encompasses scenes constituted by arbitrary configurations of real and virtual objects. Thus, we introduce the term Audio Augmented Virtuality (AAV) to describe a fully virtual environment consisting of auditory content captured from the real world, augmented by synthetic sound generation. We propose an experimental design in AAV investigating how simplified late reverberation (LR) affects the co-immersion of a sound source. Participants listened to simultaneous virtual speakers dynamically rendered through spatial Room Impulse Responses, and were asked to detect the presence of an impostor, i.e., a speaker rendered with one of two simplified LR conditions. Detection rates were found to be close to chance level, especially for one condition, suggesting a limited influence on co-immersion of the simplified LR in the evaluated AAV scenes. This methodology can be straightforwardly extended and applied to different acoustics scenes, complexities, i.e., the number of simultaneous speakers, and rendering parameters in order to further investigate the requirements for immersive audio technologies in AAR and AAV applications.

Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without centralizing raw data, and has recently received growing interest primarily as a solution to improve privacy guarantees for end users while still distilling knowledge from a population of devices (e.g., edge devices or edge gateways managing a local set of visiting devices). However, the performance of FL algorithms significantly drops in presence of heterogeneous data distributions among the learners in the federation – this setting is very common in real practical applications, with clients holding data related to their habits, preferences, or environment. Several algorithms have been recently proposed to try to deal with data heterogeneity in FL settings under different assumptions and with differentiated pros/cons. In this article, we originally provide a review of the most relevant related solutions in the literature to alleviate the harmfulness of non-identically and independently distributed (IID) data, highlighting the intuition behind these alternative strategies as well as their possible drawbacks. Furthermore, we propose an empirical comparison among a subset of such state-of-the-art solutions under different levels of data hetero-geneity running them in the same operating conditions. We end up identifying the most promising approaches considering both empirical performances and defining characteristics (e.g., assumptions the strategy possibly make). The code is available online at https://github.com/alessiomora/fI_algorithms_non_iid.