25 citations · 46 across the 19 of their papers we have counts for
6 papers · 1 filter
On the Usefulness of Diffusion-Based Room Impulse Response Interpolation to Microphone Array Processing
Sagi Della Torre, Mirco Pezzoli, Fabio Antonacci +1
Room Impulse Responses estimation is a fundamental problem in spatial audio processing and speech enhancement. In this paper, we build upon our previously introduced diffusion-base…
Phase-Retrieval-Based Physics-Informed Neural Networks For Acoustic Magnitude Field Reconstruction
Karl Schrader, Shoichi Koyama, Tomohiko Nakamura +1
We propose a method for estimating the magnitude distribution of an acoustic field from spatially sparse magnitude measurements. Such a method is useful when phase measurements are…
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models
Sagi Della Torre, Mirco Pezzoli, Fabio Antonacci +1
Room Impulse Responses (RIRs) characterize acoustic environments and are crucial in multiple audio signal processing tasks. High-quality RIR estimates drive applications such as vi…
Towards HRTF Personalization using Denoising Diffusion Models
Juan Camilo Albarracín Sánchez, Luca Comanducci, Mirco Pezzoli +1
Head-Related Transfer Functions (HRTFs) have fundamental applications for realistic rendering in immersive audio scenarios. However, they are strongly subject-dependent as they var…
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…
Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network
Marco Olivieri, Mirco Pezzoli, Fabio Antonacci +1
Near-field Acoustic Holography (NAH) is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements. In this paper, we…