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Physics-Informed Machine Learning for Sound Field Estimation: Fundamentals, state of the art, and challenges [Special Issue On Model-Based and Data-Driven Audio …
S Koyama, JGC Ribeiro, T Nakamura… - IEEE Signal …, 2025 - ieeexplore.ieee.org
The area of study concerning the estimation of spatial sound, ie, the distribution of a physical
quantity of sound such as acoustic pressure, is called sound field estimation, which is the …
quantity of sound such as acoustic pressure, is called sound field estimation, which is the …
Physics-informed machine learning for sound field estimation
The area of study concerning the estimation of spatial sound, ie, the distribution of a physical
quantity of sound such as acoustic pressure, is called sound field estimation, which is the …
quantity of sound such as acoustic pressure, is called sound field estimation, which is the …
A Zero-Shot Physics-Informed Dictionary Learning Approach for Sound Field Reconstruction
Sound field reconstruction aims to estimate pressure fields in areas lacking direct
measurements. Existing techniques often rely on strong assumptions or face challenges …
measurements. Existing techniques often rely on strong assumptions or face challenges …
A physics-informed neural network-based approach for the spatial upsampling of spherical microphone arrays
Spherical microphone arrays are convenient tools for capturing the spatial characteristics of
a sound field. However, achieving superior spatial resolution requires arrays with numerous …
a sound field. However, achieving superior spatial resolution requires arrays with numerous …
Sound field estimation using deep kernel learning regularized by the wave equation
In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-
based sound field estimation. Notably, GPs have the attractive property that the sound field …
based sound field estimation. Notably, GPs have the attractive property that the sound field …
Point neuron learning: a new physics-informed neural network architecture
Abstract Machine learning and neural networks have advanced numerous research
domains, but challenges such as large training data requirements and inconsistent model …
domains, but challenges such as large training data requirements and inconsistent model …
A broadband modeling method for range-independent underwater acoustic channels using physics-informed neural networks
Z Huang, L An, Y Ye, X Wang, H Cao, Y Du… - The Journal of the …, 2024 - pubs.aip.org
Accurate broadband modeling of underwater acoustic channels is vital for underwater
acoustic detection, localization, and communication. Conventional modeling methodologies …
acoustic detection, localization, and communication. Conventional modeling methodologies …
Reconstruction of directional sources using physics-informed neural networks
In the context of augmented/virtual reality applications, replicating the directivity of sound
sources is a critical step to ensure a high-quality immersive experience. However, accurate …
sources is a critical step to ensure a high-quality immersive experience. However, accurate …
[PDF][PDF] Milestone M7 Review and results validation on all cabin noise control system–decision on integration strategy WP2–Noise reduction in vehicle and aircraft …
The IN-NOVA project is dedicated to develop and provide knowledge and solutions in the
field of noise reduction. Specifically, it aims to develop innovative and effective methods of …
field of noise reduction. Specifically, it aims to develop innovative and effective methods of …
[PDF][PDF] Attentive Convolutional Network for Far-field Sound Reconstruction using Near-field Measurement
The far field refers to the acoustic region far from the sound source, however, accurate
measurement of the far-field sound often requires an appropriate placement of mics and …
measurement of the far-field sound often requires an appropriate placement of mics and …