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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Related Experiment Video

Updated: May 6, 2026

Three-dimensional Optical-resolution Photoacoustic Microscopy
08:31

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

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Sound-field-projection synthesis using latent diffusion model for acousto-optic reconstruction.

Risako Tanigawa1,2, Kenji Ishikawa1, Noboru Harada1

  • 1Communication Science Laboratories, NTT, Inc., 3-1 Morinosato-Wakamiya, Atsugi, Kanagawa 243-0198, Japan.

The Journal of the Acoustical Society of America
|May 5, 2026
PubMed
Summary
This summary is machine-generated.

Acousto-optic sensing (AOS) reconstructs 3D sound fields by synthesizing missing projection data. This novel approach improves volumetric sound-field reconstruction accuracy using AI-driven sound projection synthesis.

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Area of Science:

  • Acoustics
  • Optical Sensing
  • Artificial Intelligence

Background:

  • Acousto-optic sensing (AOS) offers non-contact sound measurement but provides line-integrated data, hindering 3D sound field interpretation.
  • Current volumetric sound-field reconstruction methods require extensive multi-directional data, often necessitating multiple devices or reproducible sound sources.

Purpose of the Study:

  • To develop a method for synthesizing sound projection data to overcome limitations in existing 3D sound field reconstruction techniques.
  • To improve the accuracy and practicality of volumetric sound-field reconstruction using acousto-optic sensing.

Main Methods:

  • Proposed sound projection synthesis using a latent diffusion model conditioned on observed projections and view angles.
  • Fine-tuned a pretrained image model on sound-field data, optimizing with pixel-wise loss.
  • Integrated synthesized projection data with observed data for 3D reconstruction.

Main Results:

  • The latent diffusion model successfully generated realistic sound-field projection data.
  • Combining nine observed views with nine synthesized views significantly improved 3D reconstruction accuracy compared to using only observed views.

Conclusions:

  • Sound projection synthesis is a viable method to enhance volumetric sound-field reconstruction from acousto-optic sensing data.
  • The proposed AI-driven approach offers a more practical and accurate solution for 3D sound field mapping in challenging environments.