Tweaking autoregressive methods for inpainting of gaps in audio signals

TL;DR

This paper introduces a new variant of the Janssen method for audio inpainting, demonstrating its superiority over existing autoregressive models through experiments and listening tests.

Contribution

It presents a novel gap-wise Janssen approach for audio inpainting and analyzes the impact of model estimator, window length, and model order.

Findings

Proposed Janssen variant outperforms other AR-based methods based on objective metrics.

Choice of AR estimator, window length, and model order significantly affects inpainting quality.

Listening tests confirm the effectiveness of the proposed method.

Abstract

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.