Inpainting Autoregressive
Tweaking autoregressive methods for inpainting of gaps in audio signals
Ondřej Mokrý and Pavel Rajmic, presented at EUSIPCO 2025.
A novel variant of the Janssen method for audio inpainting is presented and compared to other popular audio inpainting methods based on autoregressive (AR) modeling. Both conceptual differences and practical implications are discussed. The experiments demonstrate the importance of the choice of the AR model estimator, window/context length, and model order. The results show the superiority of the proposed gap-wise Janssen approach using objective metrics, which is confirmed by a listening test.
The preprint is available at arXiv, the official version is published at IEEE Xplore.
Supplementary plots Statistical testing of LPC versus Burg algorithm Audio examples
Reviving Etter method for autoregressive inpainting: Generalization, evaluation, implementation
Ondřej Mokrý, Matěj Hrdlička and Pavel Rajmic.
Audio inpainting aims to restore missing segments in an audio waveform, as encountered in dropouts and packet losses. This paper revisits the autoregressive (AR) interpolation method proposed by Etter, which combines forward and backward AR prediction through a structured linear system, yet lacks a widely used full implementation for audio signals. We provide an open implementation and propose two practical extensions: A formulation that allows high-order AR models even when the gap length is shorter than the model order, and a causal variant tailored to packet loss concealment. The method is evaluated on musical excerpts with gaps up to 80 ms, using SNR and perceptually motivated objective difference grades, complemented by a listening test. The results show that Etter inpainting matches the performance of most AR- and sparsity-based baseline methods, with the exception of the iterative, gap-wise Janssen approach.
The preprint is available at arXiv.