https://hal.archives-ouvertes.fr/hal-03024116
Tip revision: 1747d2d1bf483a8938562b068438a55a12ae01fc authored by Software Heritage on 01 January 2019, 00:00:00 UTC
hal: Deposit 1223 in collection hal
hal: Deposit 1223 in collection hal
Tip revision: 1747d2d
Readme.txt
% Readme.txt
%
% SPADE Toolbox
%
% ************************************************************
% This software is available under the BSD-3-Clause License (https://opensource.org/licenses/BSD-3-Clause)
% ************************************************************
% 3-Clause BSD License
%
% Copyright <2016-2019><INRIA - CNRS><Clément Gaultier, Srdan Kitic, Ewen Camberlein, Rémi Gribonval, Nancy Bertin>
%
% Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
%
% (i) Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
%
% (ii) Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
%
% (iii) Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
%
% Contact: nancy.bertin@irisa.fr, frederic.bimbot@irisa.fr, remi.gribonval@inria.fr
% PANAMA Research Group, IRISA - (Inria & CNRS)
% Campus de Beaulieu, 35042 Rennes cedex, France
How to use the SPADE Toolbox to reproduce denoising and declipping experiments?
place your clean audio files in the CleanAudioData folder
run the demo script in the Demos folder:
runDemo.m
parameters are:
CleanFile - the filename of your clean audio recording
SDR/SNR - the degradation level
param.Pb - the reconstruction problem ('Declipping' or 'Denoising')
param.window_length - the window size in seconds as the methods perform frame-based processing
param.prior - the modeling prior ('analysis' or 'synthesis')
param.Shrinkage - the sparsifying opertor ('HT' or 'PEW')
param.transform - the frequency transform used for analysis operator / dictionary (DFT or DCT)
param.redundancy - the redundancy of the frequency transform
param.accuracy - the convergence threshold for the algorithms to stop
param.loud - the waitbar activation (put 0 to deactivate)
(extra parameters can be set in ./Source/Core/runAudioRestorationExperiment.m)
Look at your results (audio files and .mat) in the Results folder
This demo code allows you to reproduce experiments from the following publications:
[1] Gaultier, C., Kitić, S., Bertin, N., & Gribonval, R. (2017, August). AUDASCITY: AUdio denoising by adaptive social CosparsITY. In 2017 25th European Signal Processing Conference (EUSIPCO) (pp. 1265-1269). IEEE.
[2] Gaultier, C., Kitić, S., Bertin, N., & Gribonval, R. Cosparse Denoising: The Importance of Being Social. The Signal Processing with Adaptive Sparse Structured Representations (SPARS) workshop, Jun 2017, Lisbon, Portugal.
[3] Gaultier, C., Bertin, N., Kitić, S., & Gribonval, R. A modeling and algorithmic framework for (non)social (co)sparse audio restoration, Research report
[4] Gaultier, C., Design and evaluation of sparse models and algorithms for audio inverse problems, PhD, University of Rennes 1
[5] Gaultier, C., Kitić, S., Gribonval, R., & Bertin, N. Sparsity-based audio declipping methods: seclected overview, new algorithms, and large-scale evaluation