1 citations · 2 across the 7 of their papers we have counts for
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Sound Field Reconstruction Using Physics-Informed Boundary Integral Networks
Stefano Damiano, Toon van Waterschoot
Sound field reconstruction refers to the problem of estimating the acoustic pressure field over an arbitrary region of space, using only a limited set of measurements. Physics-info…
The trajectoRIR Database: Room Acoustic Recordings Along a Trajectory of Moving Microphones
Stefano Damiano, Kathleen MacWilliam, Valerio Lorenzoni +2
Data availability is essential in the development of acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse trainin…
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…
Frequency Tracking Features for Data-Efficient Deep Siren Identification
Stefano Damiano, Thomas Dietzen, Toon van Waterschoot
The identification of siren sounds in urban soundscapes is a crucial safety aspect for smart vehicles and has been widely addressed by means of neural networks that ensure robustne…
Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?
Stefano Damiano, Luca Bondi, Shabnam Ghaffarzadegan +2
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effective…
Real-Time Acoustic Perception for Automotive Applications
Jun Yin, Stefano Damiano, Marian Verhelst +2
In recent years the automotive industry has been strongly promoting the development of smart cars, equipped with multi-modal sensors to gather information about the surroundings, i…