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.

Janssen 2.0: Audio Inpainting in the Time-frequency Domain

The paper focuses on inpainting missing parts of an audio signal spectrogram. First, a recent successful approach based on an untrained neural network is revised and its several modifications are proposed, improving the signal-to-noise ratio of the restored audio. Second, the Janssen algorithm, the autoregression-based state-of-the-art for time-domain audio inpainting, is adapted for the time-frequency setting. This novel method, coined Janssen-TF, is compared to the neural network approach using both objective metrics and a subjective listening test, proving Janssen-TF to be superior in all the considered measures.