Solving room impulse response inverse problems using flow matching with analytic Wiener denoisera).

Area of Science:

• Acoustics and Signal Processing
• Machine Learning and Bayesian Inference

Background:

• Room Impulse Response (RIR) estimation is crucial for acoustic analysis and involves inverse problems like denoising and deconvolution.
• Current supervised learning methods often require extensive training data and struggle with generalization beyond their training distribution.

Purpose of the Study:

• To develop a training-free Bayesian framework for RIR estimation using flow matching.
• To introduce a data-efficient and robust method for solving RIR inverse problems.

Main Methods:

• Developed RIRFlow, a Bayesian framework leveraging flow matching for RIR inverse problems.
• Derived a flow-consistent analytic prior from RIR statistical properties, modeling RIR as a Gaussian process with exponentially decaying variance.
• Integrated a closed-form Wiener denoiser as a prior into a flow-based inverse solver for guided posterior sampling.
• Extended the solver to handle nonlinear and non-Gaussian inverse problems using local Gaussian approximation.

Main Results:

• The framework successfully estimates RIRs without requiring data-driven priors or extensive training.
• Demonstrated robust performance on real RIR data across various inverse problems.
• The local Gaussian approximation proved effective for nonlinear and non-Gaussian scenarios.

Conclusions:

• RIRFlow offers an effective training-free solution for RIR inverse problems by combining classic RIR modeling with advanced generative inference.
• The approach enhances generalization and reduces data dependency compared to existing methods.