%	Readme.txt
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%								SPADE Toolbox
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% Copyright <2016-2019><INRIA - CNRS><Clément Gaultier, Srdan Kitic, Ewen Camberlein, Rémi Gribonval, Nancy Bertin>
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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