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

The so-called audio inpainting problem in the time domain refers to estimating missing segments of samples within a signal. Over the years, several methods have been developed for such type of audio inpainting. In contrast to this case, a time-frequency variant of inpainting appeared in the literature, where the challenge is to reconstruct missing spectrogram columns with reliable information. We propose a method to address this time-frequency audio inpainting problem. Our approach is based on the recently introduced phase-aware signal prior that exploits an estimate of the instantaneous frequency. An optimization problem is formulated and solved using the generalized Chambolle-Pock algorithm. The proposed method is evaluated both objectively and subjectively against other time-frequency inpainting methods, specifically a deep-prior neural network and the autoregression-based approach known as Janssen-TF. Our proposed approach surpassed these methods in the objective evaluation as well as in the conducted listening test. Moreover, this outcome is achieved with a substantially reduced computational requirement compared to alternative methods.