Near field Acoustic Holography on arbitrary shapes using Convolutional
Neural Network
- URL: http://arxiv.org/abs/2103.16935v1
- Date: Wed, 31 Mar 2021 09:41:11 GMT
- Title: Near field Acoustic Holography on arbitrary shapes using Convolutional
Neural Network
- Authors: Marco Olivieri, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti
- Abstract summary: Near-field Acoustic Holography is a well-known problem aimed at estimating the vibrational velocity field of a structure by means of acoustic measurements.
We propose a NAH technique based on Convolutional Neural Network (CNN)
We validate the proposed method by comparing the estimates with the synthesized ground truth and with a state-of-the-art technique.
- Score: 9.1673176404097
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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 propose a NAH technique based on Convolutional
Neural Network (CNN). The devised CNN predicts the vibrational field on the
surface of arbitrary shaped plates (violin plates) with orthotropic material
properties from a limited number of measurements. In particular, the
architecture, named super resolution CNN (SRCNN), is able to estimate the
vibrational field with a higher spatial resolution compared to the input
pressure. The pressure and velocity datasets have been generated through Finite
Element Method simulations. We validate the proposed method by comparing the
estimates with the synthesized ground truth and with a state-of-the-art
technique. Moreover, we evaluate the robustness of the devised network against
noisy input data.
Related papers
- Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain
State Decoding [0.0]
We propose a two-stage computational framework combining Hopfield Networks for artifact data preprocessing with Conal Neural Networks (CNNs) for classification of brain states in rat neural recordings under different levels of anesthesia.
Performance across various levels of data compression and noise intensities showed that our framework can effectively mitigate artifacts, allowing the model to reach parity with the clean-data CNN at lower noise levels.
arXiv Detail & Related papers (2023-11-06T15:08:13Z) - Neural Acoustic Context Field: Rendering Realistic Room Impulse Response
With Neural Fields [61.07542274267568]
This letter proposes a novel Neural Acoustic Context Field approach, called NACF, to parameterize an audio scene.
Driven by the unique properties of RIR, we design a temporal correlation module and multi-scale energy decay criterion.
Experimental results show that NACF outperforms existing field-based methods by a notable margin.
arXiv Detail & Related papers (2023-09-27T19:50:50Z) - Generative adversarial networks with physical sound field priors [6.256923690998173]
This paper presents a deep learning-based approach for learns-temporal reconstruction of sound fields using Generative Adversa Networks (GANs)
The proposed method uses a plane wave basis and the underlying statistical distributions of pressure in rooms to reconstruct sound fields from a limited number of measurements.
The results suggest that this approach provides a promising approach to sound field reconstruction using generative models that allow for a physically informed acoustics prior to problems.
arXiv Detail & Related papers (2023-08-01T10:11:23Z) - Neural Modulation Fields for Conditional Cone Beam Neural Tomography [18.721488634071193]
Cone Beam Neural Tomography (CondCBNT) shows improved performance for both high and low numbers of available projections on noise-free and noisy data.
We propose a novel conditioning method where local modulations are modeled per patient as a field over the input domain through a Neural Modulation Field (NMF)
arXiv Detail & Related papers (2023-07-17T09:41:01Z) - Pre-training via Denoising for Molecular Property Prediction [53.409242538744444]
We describe a pre-training technique that utilizes large datasets of 3D molecular structures at equilibrium.
Inspired by recent advances in noise regularization, our pre-training objective is based on denoising.
arXiv Detail & Related papers (2022-05-31T22:28:34Z) - Estimating permeability of 3D micro-CT images by physics-informed CNNs
based on DNS [1.6274397329511197]
This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples.
The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM)
We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner.
arXiv Detail & Related papers (2021-09-04T08:43:19Z) - Simultaneous boundary shape estimation and velocity field de-noising in
Magnetic Resonance Velocimetry using Physics-informed Neural Networks [70.7321040534471]
Magnetic resonance velocimetry (MRV) is a non-invasive technique widely used in medicine and engineering to measure the velocity field of a fluid.
Previous studies have required the shape of the boundary (for example, a blood vessel) to be known a priori.
We present a physics-informed neural network that instead uses the noisy MRV data alone to infer the most likely boundary shape and de-noised velocity field.
arXiv Detail & Related papers (2021-07-16T12:56:09Z) - Self-Learning for Received Signal Strength Map Reconstruction with
Neural Architecture Search [63.39818029362661]
We present a model based on Neural Architecture Search (NAS) and self-learning for received signal strength ( RSS) map reconstruction.
The approach first finds an optimal NN architecture and simultaneously train the deduced model over some ground-truth measurements of a given ( RSS) map.
Experimental results show that signal predictions of this second model outperforms non-learning based state-of-the-art techniques and NN models with no architecture search.
arXiv Detail & Related papers (2021-05-17T12:19:22Z) - Deep Networks for Direction-of-Arrival Estimation in Low SNR [89.45026632977456]
We introduce a Convolutional Neural Network (CNN) that is trained from mutli-channel data of the true array manifold matrix.
We train a CNN in the low-SNR regime to predict DoAs across all SNRs.
Our robust solution can be applied in several fields, ranging from wireless array sensors to acoustic microphones or sonars.
arXiv Detail & Related papers (2020-11-17T12:52:18Z) - Prediction of Object Geometry from Acoustic Scattering Using
Convolutional Neural Networks [8.067201256886733]
The present work proposes to infer object geometry from scattering features by training convolutional neural networks.
The robustness of our approach in response to data degradation is evaluated by comparing the performance of networks trained using the datasets.
arXiv Detail & Related papers (2020-10-21T00:51:14Z) - Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by
Spiking Neural Network [68.43026108936029]
We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment.
We implement this algorithm in a real-time robotic system with a microphone array.
The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
arXiv Detail & Related papers (2020-07-07T08:22:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.