Audio Inpainting in Time-Frequency Domain with Phase-Aware Prior

Abstract

We address the problem of time-frequency audio inpainting, where the goal is to fill missing spectrogram portions with consistent information. Despite recent advances, existing approaches still face limitations in both reconstruction quality and computational efficiency. To bridge this gap, we propose a method that utilizes a phase-aware signal prior which exploits estimates of the instantaneous frequency. An optimization problem is formulated and solved using the generalized Chambolle-Pock algorithm. The proposed method is evaluated against other time-frequency inpainting methods, specifically a deep-prior audio inpainting neural network, the autoregression-based approach known as Janssen-TF, and a sparsity-driven baseline. For short gap durations, the proposed approach achieves superior SNR, while performing comparably to Janssen-TF on larger gaps. In terms of perceptual quality (both objective and subjective), the proposed method consistently outperforms existing methods across all gap lengths. In addition, the reconstructions are obtained with a substantially reduced computational cost compared to alternative methods.