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赫尔辛基科技大学有关于盲信号去噪的的matlab工具箱,非常好用。希望对大家有用
hitu-denoise.rar
  • 赫尔辛基科技大学去噪函数库
  • beta-6版
  • src
  • testkeypress.c
    867B
  • Keytest.java
    248B
  • demo
  • example.mat
    320.2KB
  • demos
  • spectplot.m
    527B
  • DSSlapkoe.m
    8.9KB
  • demo_lap.m
    1.3KB
  • demo_low.m
    1.6KB
  • ringplot2.m
    2.1KB
  • test
  • test_basic_symm.m
    380B
  • create_mixed_source.m
    672B
  • test_ws_initial_symm.m
    627B
  • create_test_signals.m
    782B
  • test_predictive_gamma_symm.m
    160B
  • test_est.m
    527B
  • test_ws_initial.m
    897B
  • run_tests.m
    1016B
  • test_basic_defl.m
    107B
  • test_sphere.m
    2.3KB
  • test_meg.m
    598B
  • gui
  • dss_gui_advOptions.fig
    4KB
  • dss_gui.m
    77.8KB
  • dss_gui_insertFunc.fig
    1.7KB
  • dss_gui_save.fig
    2.1KB
  • dss_gui_funcParams.fig
    2.9KB
  • dss_gui_advOptions.m
    37.2KB
  • dss_gui_modalDlg.fig
    5.7KB
  • README_GUI.txt
    4.4KB
  • dss_gui.fig
    8.6KB
  • dss_gui_funcParams.m
    25.1KB
  • dss_gui_about.fig
    1.8KB
  • dss_gui_about.m
    3.7KB
  • dss_gui_browse.fig
    2.1KB
  • dss_gui_save.m
    8.3KB
  • dss_gui_help.m
    4.9KB
  • dss_gui_modalDlg.m
    7.6KB
  • dss_gui_browse.m
    24KB
  • dss_gui_insertFunc.m
    12.2KB
  • report_print.m
    908B
  • denoise_dct.m
    1006B
  • estimate_period.m
    861B
  • dss_check_adaptivity.m
    2KB
  • dss_fastica.m
    943B
  • LICENSE
    17.6KB
  • pre_sphere_symm.m
    852B
  • dss_2dmask.m
    907B
  • gamma_179.m
    1KB
  • message.m
    577B
  • dss_core_pca.m
    1.2KB
  • CHANGES
    1.2KB
  • report_w.m
    419B
  • denoise_filter.m
    2.1KB
  • dss.m
    1.7KB
  • Keytest.class
    442B
  • prepare.m
    515B
  • estimate_mask.m
    1.7KB
  • beta_global.m
    962B
  • ortho_default.m
    969B
  • denoise_fica_tanh.m
    1.2KB
  • default_stop.m
    1.5KB
  • denoise_tanh.m
    1.1KB
  • denoise_energy.m
    2.6KB
  • dss_create_state.m
    8KB
  • dss_core_symm.m
    4.2KB
  • dss_core_defl.m
    5.4KB
  • denoise_fica_kurtosis.m
    1002B
  • logplot.m
    885B
  • report_objective.m
    549B
  • beta_tanh.m
    898B
  • randlap.m
    528B
  • gamma_predictive.m
    1KB
  • dss_set_denoising.m
    1.3KB
  • sphere.m
    2KB
  • ortho_quasi.m
    1.3KB
  • denoise_avg.m
    2.5KB
  • dss_preprocess.m
    872B
  • denoise_fica_gauss.m
    1.2KB
  • pre_sphere.m
    827B
  • denoise_mask.m
    1.1KB
  • denoise_pow3.m
    1.1KB
  • denoise_fica_skew.m
    974B
  • gamma_predictive_symm.m
    1.1KB
  • README.txt
    8.1KB
  • denoise_smooth_tanh.m
    1.3KB
  • report_convergence.m
    1.5KB
  • 1-0版
  • src
  • testkeypress.c
    867B
  • Keytest.java
    248B
  • demos
  • demo_MEGprior.m
    4KB
  • demo_MEGblind.m
    1.6KB
  • art_data.mat
    320.2KB
  • demo_art.m
    1.9KB
内容介绍
DSS MATLAB package Copyright (C) 2004, 2005 DSS MATLAB package team (dss@cis.hut.fi). Distributed by Laboratory of Computer and Information Science, Helsinki University of Technology. http://www.cis.hut.fi/projects/dss/. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. REQUIREMENTS This is a MATLAB package. The command line version requires MATLAB v6.1 and the graphical user interface v7. INSTALLATION Copy archive into proper directory and unpack it > gtar -xvzf dss_package.tar.gz Add DSS package (directory where 'dss.m' is located) to the Matlab path. Use absolute path definition. a) Use Matlab pathtool to add path. OR b) Add line to the startup.m in your home directory addpath('/path/to/dss') OR c) Add item to the p vector in ~/.matlab/pathdef.m '/path/to/dss:',... If you want to use keyboard to interrupt calculation (so that it can be continued afterwards), MEX or java function must be set to handle keypresses. To set up java keyboard interrupt modify 'classpath.txt' file and add path to dss directory on a new line or set the path using MATLABs javaclasspath command. To set up MEX keyboard interrupt a testkeypress-function must be compiled with 'prepare.m' script. This requires that MEX compiler environment is properly set up. GRAPHICAL USER INTERFACE DSS package contains a graphical user interface (GUI). See README in gui-directory for more information. USAGE DSS can be run with default parameters simply by passing the mixed signal matrix as a single parameter. The result is the state structure containing the details of the run. The state structure allows long calculations to be interrupted and continued later on. The algorithm also copies the essential results to the other output arguments. >> [state] = denss(X) >> [state, W, A, ...] = denss(X) The algorithm can be interrupted by a control user interface or pressing the SPACE-key and choosing the proper interrupt option. After interruption, the calculation can be continued by giving the state structure as a parameter. >> [state, ...] = denss(state) The parameters are given to the algorithm in a parameter structure that is similar to the state structure. Parameters can be also given as a cell array containing parameter and value pairs. The initialization of the state script can be done in a separate initialization function. Also an existing state structure can be updated with new parameters. % create new parameter structure, set the source dimension to 3 >> params.sdim = 3 % use symmetric approach >> params.algorithm = 'symm' % run algorithm with existing parameter structure >> [state, ...] = denss(X, params) % run algorithm by passing parameters directly >> [state, ...] = denss(X, {'sdim', 3, 'algorithm', 'symm', ...}) % create algorithm state structure from parameter structure >> state = dss_create_state(X, params) % run algorithm with previously created state structure or % continue interrupted calculation >> [state, ...] = denss(state) % update the algorithm state structure with new parameters >> state = dss_create_state(state, params) % run algorithm with existing state structure together with new parameters >> [state, ...] = denss(state, params) Some description of the parameters can be found from the files denss.m and dss_init.m. DSS algorithm customization is made mostly by providing custom functions for various algorithm operations. See the demos in the demos directory for more information. NOTES MATLAB uses a clever pass-by-reference-or-value in function calls. This means that passed variables are not copied (which would take some CPU-time) if the function does not change them. For this reason, one should avoid changing e.g. the state variable in the denoising functions, even if it is not returned (no change would be visible in the main program). Instead, make local copies of the relevant parts in the denoising function. If you have run the dss algorithm once and would like to run it again, you have to clear the fields 'S' and 'W' from the state struct. Otherwise, dss will start from the predefined S (or W). FILES --Main DSS functions: denss.m Main script for running DSS algorithm. Creates state structure and calls defl/symm implementation dss_fastica.m DSS with FastICA-like interface dss_2dmask.m 2-dim mask denoising (eg. spectrogram denoising) dss_create_state.m Creates DSS state struct based on given parameters dss_set_denoising.m Initialize denoising function and parameters estimate_mask.m Estimate bit mask based on signal dynamics prepare.m Initialize environment (compile testkeypress.mex) --Internal functions: dss_preprocess.m Performs data preprocessing dss_check_adaptivity.m Initialize alpha, beta and gamma values or functions dss_core_defl.m Deflation DSS core dss_core_symm.m Symmetric DSS core dss_core_pca.m PCA DSS core message.m Print message depending on verbosity level --Helper functions: randlap.m Random laplacian noise logplot.m Plot data in linear & logarithmic scale Keytest.class Keyboard reading for interrupting DSS from keyboard --Preprocessing functions: pre_sphere.m Default whitening function (basic PCA whitening) pre_sphere_symm.m Symmetric sphering --Stopping functions: default_stop.m Default stopping criteria function --Orthogonalization functions: ortho_default.m Default orthogonalization function (both defl & symm) ortho_quasi.m Quasiorthogonalization --Denoising functions: denoise_fica_gauss.m Equals FastICA gaussian non-linearity denoise_fica_kurtosis.m Equals FastICA kurtosis non-linearity denoise_fica_skew.m Equals FastICA skewness non-linearity denoise_fica_tanh.m Equals FastICA tanh non-linearity (default) denoise_pow3.m Kurtosis denoising function denoise_tanh.m Tanh denoising function denoise_mask.m Simple mask denoising function denoise_energy.m Energy based denoising denoise_smooth_tanh.m Smooth tanh denoising denoise_filter.m Generic signal filter (also a denoising function) denoise_dct.m denoise_avg.m Ensemble average denoising using given triggers --Alpha functions: *none --Beta functions: beta_global.m Global spectral shift for all denoising functions beta_tanh.m Adaptive (local) spectral shift for tanh denoising beta_pow3.m Adaptive (local) spectral shift for pow3 denoising, equals kurtosis extremisation --Gamma functions: gamma_179.m 179-rule adaptive gamma gamma_predictive.m Predictive controller adaptive gamma (defl dss) gamma_predictive_symm.m Predictive controller adaptive gamma (symm dss) --Reporting functions: report_convergence.m report_objective.m report_print.m report_w.m --Directories: gui DSS GUI src Misc source files test Unit tests demos Demonstration data and scripts CALLBACK FUNCTION SIGNATURES Preprocessing: [params, Y, wM, dwM] = preprocf(params, X, dim) params Function specific parameters X Original data Y Whitened data wM Whitening matrix
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