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VR-PTOLEMAIC: A Virtual Environment for the Perceptual Testing of Spatial Audio Algorithms
Paolo Ostan, Francesca Del Gaudio, Federico Miotello +2
The perceptual evaluation of spatial audio algorithms is an important step in the development of immersive audio applications, as it ensures that synthesized sound fields meet qual…
Low-Rank Adaptation of Deep Prior Neural Networks For Room Impulse Response Reconstruction
Mirco Pezzoli, Federico Miotello, Shoichi Koyama +1
The Deep Prior framework has emerged as a powerful generative tool which can be used for reconstructing sound fields in an environment from few sparse pressure measurements. It emp…
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
Stefano Damiano, Federico Miotello, Mirco Pezzoli +4
Sound field reconstruction aims to estimate pressure fields in areas lacking direct measurements. Existing techniques often rely on strong assumptions or face challenges related to…
A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays
Federico Miotello, Ferdinando Terminiello, Mirco Pezzoli +3
Spherical microphone arrays are convenient tools for capturing the spatial characteristics of a sound field. However, achieving superior spatial resolution requires arrays with num…
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
Federico Miotello, Paolo Ostan, Mirco Pezzoli +4
In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to m…
Reconstruction of Sound Field through Diffusion Models
Federico Miotello, Luca Comanducci, Mirco Pezzoli +3
Reconstructing the sound field in a room is an important task for several applications, such as sound control and augmented (AR) or virtual reality (VR). In this paper, we propose…