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  3. A Conditional Diffusion-based Model For High-resolution Acoustic Source Mapping.
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  3. A Conditional Diffusion-based Model For High-resolution Acoustic Source Mapping.

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A conditional diffusion-based model for high-resolution acoustic source mapping.

Haobo Jia1,2, Feiran Yang3, Jianfei Tong1

  • 1Laboratory of Noise and Audio Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America
|March 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel diffusion-based framework for acoustic source mapping, improving deconvolution accuracy. The generative model effectively captures source structures, outperforming existing methods in generalization tasks.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Inverse imaging problems, particularly acoustic source mapping, are challenging due to sparse and peak-shaped source distributions.
  • Traditional supervised regression methods struggle to accurately model these complex source structures, often resulting in blurry artifacts.
  • Diffusion models offer powerful generative capabilities applicable to inverse problems.

Purpose of the Study:

  • To introduce the first diffusion-based framework for acoustic source mapping that directly addresses the deconvolution inverse problem.
  • To develop a generative model that explicitly learns the structural prior of source maps, avoiding blurry artifacts.
  • To enhance acoustic source mapping accuracy and generalization capabilities.

Main Methods:

  • A diffusion-based generative framework conditioned on delay-and-sum beamforming maps and autoencoder-extracted multi-scale point spread function features.
  • Utilizing a smoothed target map to guide the model in capturing structural priors.
  • Implementing a time-weighted loss function to improve condition exploitation during training.
  • Employing an autoencoder to extract frequency-aware features from point spread functions.

Main Results:

  • The proposed diffusion model successfully generates high-resolution acoustic source distribution maps with only 20 sampling steps during inference.
  • Experimental results demonstrate superior performance compared to traditional and supervised regression-based deep learning methods.
  • The framework shows strong generalization across unseen frequencies, varying numbers of sources, and real-world transfer functions.

Conclusions:

  • The diffusion-based framework represents a significant advancement in acoustic source mapping, overcoming limitations of previous methods.
  • The model's ability to learn structural priors and avoid blurry artifacts leads to more accurate and detailed source maps.
  • This approach offers a robust and generalizable solution for acoustic source mapping in complex scenarios.