A conditional diffusion-based model for high-resolution acoustic source mapping | JoVE Visualize

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.