Regularized autoregressive modeling and its application to audio signal reconstruction

TL;DR

This paper introduces a comprehensive regularized autoregressive modeling framework for audio signal reconstruction, demonstrating its effectiveness in declipping and dequantization tasks with superior performance over existing methods.

Contribution

The paper presents a novel, generic AR modeling framework with an optimization algorithm, unifying prior regularization approaches and improving audio signal reconstruction.

Findings

Outperforms state-of-the-art in audio declipping and dequantization

Demonstrates superiority in speech declipping

Shows competitive results in musical signal declipping

Abstract

Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although these attempts are appealing, an encompassing and generic modeling framework is still missing. We propose such a framework and the related optimization problem and algorithm. We discuss the computational demands of the algorithm and explore the effects of various improvements on its convergence speed. In the experimental part, we demonstrate the usefulness of our approach on the audio declipping and dequantization problems. We compare its performance against state-of-the-art methods and demonstrate the competitiveness of the proposed method in declipping musical signals, and its superiority in declipping speech. The evaluation includes a heuristic algorithm of generalized linear prediction (GLP), a strong competitor which has only been presented as a patent and is new in the scientific community.