Sparse and Cosparse Audio Dequantization Using Convex Optimization

03/05/2020
by   Pavel Záviška, et al.
0

The paper shows the potential of sparsity-based methods in restoring quantized signals. Following up on the study of Brauer et. al. (IEEE ICASSP 2016), we significantly extend the range of the evaluation scenarios: we introduce the analysis (cosparse) model, we use more effective algorithms, we experiment with another time-frequency transform. The paper shows that the analysis-based model performs comparably to the synthesis-model, but the Gabor transform produces better results than the originally used cosine transform. Last but not least, we provide codes and data in a reproducible way.

READ FULL TEXT
research
04/13/2019

Audio Compression Using Graph-based Transform

Graph-based Transform is one of the recent transform coding methods whic...
research
10/31/2018

Introducing SPAIN (SParse Audion INpainter)

A novel sparsity-based algorithm for audio inpainting is proposed by tra...
research
02/05/2021

Cosine Series Representation

This short paper is based on Chung et al. (2010), where the cosine serie...
research
08/03/2023

Versatile Time-Frequency Representations Realized by Convex Penalty on Magnitude Spectrogram

Sparse time-frequency (T-F) representations have been an important resea...
research
12/08/2022

High Quality Audio Coding with MDCTNet

We propose a neural audio generative model, MDCTNet, operating in the pe...
research
05/20/2022

Audio Declipping with (Weighted) Analysis Social Sparsity

We develop the analysis (cosparse) variant of the popular audio declippi...
research
05/07/2015

Effects of Nonparanormal Transform on PC and GES Search Accuracies

Liu, et al., 2009 developed a transformation of a class of non-Gaussian ...

Please sign up or login with your details

Forgot password? Click here to reset