4 papers
Physics-Informed Machine Learning For Sound Field Estimation
Shoichi Koyama, Juliano G. C. Ribeiro, Tomohiko Nakamura +2
The area of study concerning the estimation of spatial sound, i.e., the distribution of a physical quantity of sound such as acoustic pressure, is called sound field estimation, wh…
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…
Physics-Informed Neural Network for Volumetric Sound field Reconstruction of Speech Signals
Marco Olivieri, Xenofon Karakonstantis, Mirco Pezzoli +3
Recent developments in acoustic signal processing have seen the integration of deep learning methodologies, alongside the continued prominence of classical wave expansion-based app…
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…