Audio Dequantization Using (Co)Sparse (Non)Convex Methods

10/30/2020
by   Pavel Záviška, et al.
0

The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. The experiments evaluate the methods using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric. The analysis variants of convex approaches turn out to give the best results.

READ FULL TEXT
research
10/31/2018

Introducing SPAIN (SParse Audion INpainter)

A novel sparsity-based algorithm for audio inpainting is proposed by tra...
research
02/11/2020

IPBoost – Non-Convex Boosting via Integer Programming

Recently non-convex optimization approaches for solving machine learning...
research
01/29/2019

Rank-one Convexification for Sparse Regression

Sparse regression models are increasingly prevalent due to their ease of...
research
05/20/2022

Audio Declipping with (Weighted) Analysis Social Sparsity

We develop the analysis (cosparse) variant of the popular audio declippi...
research
02/26/2013

Convex vs nonconvex approaches for sparse estimation: GLasso, Multiple Kernel Learning and Hyperparameter GLasso

The popular Lasso approach for sparse estimation can be derived via marg...
research
05/06/2021

Inverse Scale Space Iterations for Non-Convex Variational Problems Using Functional Lifting

Non-linear filtering approaches allow to obtain decompositions of images...
research
08/09/2023

SUnAA: Sparse Unmixing using Archetypal Analysis

This paper introduces a new sparse unmixing technique using archetypal a...

Please sign up or login with your details

Forgot password? Click here to reset