Deep Prior Approach for Room Impulse Response Reconstruction.

Area of Science:

• Acoustics
• Signal Processing
• Machine Learning

Background:

• Room Impulse Response (RIR) reconstruction is crucial for acoustic system design.
• Current methods often rely on restrictive assumptions about the acoustic environment.
• A data-driven approach using deep learning offers a potential solution.

Purpose of the Study:

• To develop a novel method for reconstructing unknown Room Impulse Responses (RIRs).
• To utilize the deep prior paradigm for regularized inverse problem solving.
• To overcome limitations of state-of-the-art RIR reconstruction algorithms.

Main Methods:

• Formulating RIR reconstruction as an inverse problem.
• Employing a Convolutional Neural Network (CNN) as a deep prior.
• Utilizing per-element training for independent RIR reconstruction.

Main Results:

• Accurate RIR reconstruction demonstrated on simulated data across various scenarios (source direction, T60, SNR).
• The method shows robustness to noise in real-world acoustic measurements.
• Performance surpasses existing state-of-the-art RIR reconstruction techniques.

Conclusions:

• The proposed deep prior approach enables accurate and assumption-free RIR reconstruction.
• This method eliminates the need for extensive training datasets.
• It offers a flexible and robust solution for diverse acoustic environments.