A survey and an extensive evaluation of popular audio declipping methods

Abstract

Dynamic range limitations in signal processing often lead to clipping, or saturation, in signals. The task of audio declipping is estimating the original audio signal, given its clipped measurements, and has attracted much interest in recent years. Audio declipping algorithms often make assumptions about the underlying signal, such as sparsity or low-rankness, and about the measurement system. In this paper, we provide an extensive review of audio declipping algorithms proposed in the literature. For each algorithm, we present assumptions that are made about the audio signal, the modeling domain, and the optimization algorithm. Furthermore, we provide an extensive numerical evaluation of popular declipping algorithms, on real audio data. We evaluate each algorithm in terms of the Signal-to-Distortion Ratio, and also using perceptual metrics of sound quality. The article is accompanied by a repository containing the evaluated methods.

References in corpus (11)

• Wavelet methods in statistics: Some recent developments and their applications
• One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
• GACELA -- A generative adversarial context encoder for long audio inpainting
• Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements
• Introducing SPAIN (SParse Audio INpainter)
• A Proper version of Synthesis-based Sparse Audio Declipper
• Audio Inpainting: Revisited and Reweighted
• Estimation with Low-Rank Time-Frequency Synthesis Models
• Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
• Image to Image Translation based on Convolutional Neural Network Approach for Speech Declipping
• Declipping of Speech Signals Using Frequency Selective Extrapolation

Cited by in corpus (7)

• VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
• VoiceFixer: Toward General Speech Restoration with Neural Vocoder
• An empirical study on speech restoration guided by self supervised speech representation
• Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
• Multiple Hankel matrix rank minimization for audio inpainting
• Audio Dequantization Using (Co)Sparse (Non)Convex Methods
• Audio declipping performance enhancement via crossfading