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Version 0.0.1 - Initial Commit
author Francisco Rodriguez Algarra <f.rodriguezalgarra@qmul.ac.uk>
date Wed, 21 Oct 2015 00:02:11 +0100
parents
children 9a37f107f089
files .hgignore LICENSE.txt README.txt dss.m dss.sh dss/classification/classify.m dss/experiments/exp_mfcc_740ms.m dss/experiments/exp_time_freq_scat_l2.m dss/experiments/exp_time_freq_scat_l2_adap_q1.m dss/experiments/exp_time_scat_l1.m dss/experiments/exp_time_scat_l2.m dss/experiments/exp_time_scat_l3.m dss/folds/createFolds.m dss/folds/test_filtered.txt dss/folds/test_stratified.txt dss/folds/train_filtered.txt dss/folds/train_stratified.txt dss/folds/valid_filtered.txt dss/folds/valid_stratified.txt
diffstat 19 files changed, 2572 insertions(+), 0 deletions(-) [+]
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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+syntax: glob
+*.txt~
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE.txt	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,204 @@
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+   Copyright 2013 Stéphane Mallat, Laurent Sifre, Joakim Andén, Michel Kapoko,
+   Edouard Oyallon, and Vincent Lostanlen.
+
+   Copyright 2015 Francisco Rodríguez-Algarra, and Bob L. Sturm.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.txt	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,35 @@
+Re-Evaluation of the Scattering Transform
+Version 0.0.1
+October 2015
+
+Contact: Francisco Rodríguez-Algarra (f.rodriguez@qmul.ac.uk)
+
+* LICENSING AND ATTRIBUTION INFORMATION
+
+Copyright (c) 2013 Stéphane Mallat, Laurent Sifre, Joakim Andén, Michel Kapoko, Edouard Oyallon, and Vincent Lostanlen.
+Copyright (c) 2015 Francisco Rodríguez-Algarra, and Bob L. Sturm.
+
+For licensing information see the file named LICENSE.
+
+This code is mainly based on the code provided with the following paper:
+J. Andén, and S. Mallat, "Deep Scattering Spectrum", IEEE Transactions on Signal Processing, vol. 62, pp. 4114-4128, 2014.
+
+If you use this code, please cite the following paper:
+F. Rodríguez-Algarra and B. L. Sturm, "Re-Evaluating the Scattering Transform", in Proc. 16th International Society of Music Information Retrieval Conference (ISMIR'15), Málaga, Spain, Oct. 2015.
+
+* USAGE
+
+This code requires the following libraries:
+- ScatNet v0.2
+- Compact LIBSVM v0.1
+Both libraries can be obtained in: http://www.di.ens.fr/data/software/
+
+It also requires the GTZAN music dataset, available at: http://opihi.cs.uvic.ca/sound/genres.tar.gz
+
+In order to run the experiments, you first need to edit the file "dss.m", modifying the values of the variables "scatnet_path", "libsvm_compact_path", and "gtzan_path" to point at the locations of the libraries and the dataset in your own system.
+
+You can also modify the location where the results of the experiments will be stored changing the value of the "results_dir" variable.
+
+Once the variables have been properly modified, to run the experiments simply execute the script "dss.m" in Matlab.
+
+
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,46 @@
+% Modify the following variables to include the locations 
+% of the libraries and the dataset in your own system
+scatnet_path = [pwd, '/../libraries/scatnet-0.2/'];
+libsvm_compact_path = [pwd, '/../libraries/libsvm-compact-0.1/'];
+global gtzan_path;
+gtzan_path = '/import/c4dm-datasets/gtzan/';
+global results_dir;
+results_dir = [pwd, '/results/'];
+%
+
+fprintf('\n**********\nAdding the necessary folders to Matlab path\n**********\n');
+run([scatnet_path, 'addpath_scatnet.m']);
+addpath([libsvm_compact_path, 'matlab']);
+
+mfilepath=fileparts(which(mfilename));
+addpath(fullfile(mfilepath,'dss/experiments/'));
+addpath(fullfile(mfilepath,'dss/folds/'));
+addpath(fullfile(mfilepath,'dss/classification/'));
+
+fprintf('\n**********\nSTARTING TO RUN EXPERIMENTS\n**********\n');
+cd dss;
+
+global src;
+src = gtzan_src(gtzan_path);
+
+fprintf('\n**********\nFEATURE SET (a)\n**********\n');
+fprintf('\n**********\nMFCC, T=740ms\n**********\n');
+exp_mfcc_740ms;
+fprintf('\n**********\nFEATURE SET (b)\n**********\n');
+fprintf('\n**********\nTime Scattering, l=1\n**********\n');
+exp_time_scat_l1;
+fprintf('\n**********\nFEATURE SET (c)\n**********\n');
+fprintf('\n**********\nTime Scattering, l=2\n**********\n');
+exp_time_scat_l2;
+fprintf('\n**********\nFEATURE SET (d)\n**********\n');
+fprintf('\n**********\nTime and Frequency Scattering, l=2\n**********\n');
+exp_time_freq_scat_l2;
+fprintf('\n**********\nFEATURE SET (e)\n**********\n');
+fprintf('\n**********\nTime and Frequency Scattering, l=2, Adaptive Q1\n**********\n');
+exp_time_freq_scat_l2_adap_q1;        
+fprintf('\n**********\nFEATURE SET (f)\n**********\n');
+fprintf('\n**********\nTime Scattering, l=3\n**********\n')
+exp_time_scat_l3;
+
+quit;
+
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss.sh	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,4 @@
+#!/bin/bash
+matlab -nodisplay -nosplash -nojvm -r "
+run dss; quit;
+"
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/classification/classify.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,47 @@
+function [] = classify(db, optt, preffix)
+
+cond = cellstr(['none '; 'fault']);
+
+for ii=1:length(cond)
+
+  [train_set,test_set] = createFolds(cond{ii});
+  train_set = find(train_set)';
+  test_set = find(test_set)';
+
+  [dev_err_grid,C_grid,gamma_grid] = ...
+	  svm_adaptive_param_search(db,train_set,[],optt);
+
+  [dev_err,ind] = min(mean(dev_err_grid{end},2));
+  C = C_grid{end}(ind);
+  gamma = gamma_grid{end}(ind);
+
+  optt1 = optt;
+  optt1.C = C;
+  optt1.gamma = gamma;
+
+  model = svm_train(db,train_set,optt1);
+  labels = svm_test(db,model,test_set);
+  err = classif_err(labels,test_set,db.src);
+
+  fprintf('dev err = %f, test err = %f\n\n',dev_err,err);
+
+  % dummy renaming of variables
+  id = test_set';
+  true_label = kron((1:10), ones(1, 100));
+  true_label = true_label(test_set)';
+  pred_label = labels';
+
+  % saving results in table
+  results = table(id, true_label, pred_label)
+
+  run_name = [preffix, cond{ii}];
+
+  global results_dir;
+  [s, mess, messid] = mkdir(results_dir);
+  if(s ~= 1) exit; end;
+
+  save([results_dir, run_name, '.mat'],'dev_err','err','C','gamma', 'labels', 'results');
+  writetable(results, [results_dir, run_name, '_results.csv'])
+
+end;
+
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_mfcc_740ms.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,34 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% Delta-Delta-MFCCs for window size 740 ms
+
+preffix = 'mfcc_740ms_';
+
+N = 5*2^17;
+
+global src;
+
+filt1_opt.wavelet_type = {'gabor','morlet'};
+filt1_opt.Q = [8 2];
+filt1_opt.J = T_to_J(8192*2,filt1_opt);
+filt1_opt.boundary = 'symm';
+
+sc1_opt.M = 1;
+
+filters = filter_bank(N, filt1_opt);
+
+features = {@(x)(feval(@(x)([x; circshift(x,[0 +1]); circshift(x,[0 -1])]), ...
+	format_scat(log_scat(spec_freq_average(x,filters,sc1_opt)))))};
+
+db = prepare_database(src,features);
+db.features = single(db.features);
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 1;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_time_freq_scat_l2.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,50 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% M=2 scattering, frequency scattering
+
+preffix = 'time_freq_scat_l2_';
+
+N = 5*2^17;
+
+global src;
+
+filt1_opt.filter_type = {'gabor_1d','morlet_1d'};
+filt1_opt.Q = [8 1];
+filt1_opt.J = T_to_J(8192*2,filt1_opt);
+
+sc1_opt.M = 2;
+
+ffilt1_opt.filter_type = 'morlet_1d';
+ffilt1_opt.J = 7;
+
+fsc1_opt.M = 1;
+
+Wop1 = wavelet_factory_1d(N, filt1_opt, sc1_opt);
+fWop1 = wavelet_factory_1d(128, ffilt1_opt, fsc1_opt);
+
+scatt_fun1 = @(x)(log_scat(renorm_scat(scat(x,Wop1))));
+fscatt_fun1 = @(x)(func_output(@scat_freq,2,scatt_fun1(x),fWop1));
+feature_fun1 = @(x)(format_scat(fscatt_fun1(x)));
+
+features = {feature_fun1};
+
+for k = 1:length(features)
+    fprintf('testing feature #%d...',k);
+    tic;
+    sz = size(features{k}(randn(N,1)));
+    aa = toc;
+    fprintf('OK (%.2fs) (size [%d,%d])\n',aa,sz(1),sz(2));
+end
+
+db = prepare_database(src,features);
+db.features = single(db.features);
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 1;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_time_freq_scat_l2_adap_q1.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,68 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% M=2 scattering, frequency scattering, multiple Q1
+
+preffix = 'time_freq_scat_l2_adap_q1_';
+
+N = 5*2^17;
+
+global src;
+
+filt1_opt.filter_type = {'gabor_1d','morlet_1d'};
+filt1_opt.Q = [8 1];
+filt1_opt.J = T_to_J(8192*2,filt1_opt);
+
+sc1_opt.M = 2;
+
+ffilt1_opt.filter_type = 'morlet_1d';
+ffilt1_opt.J = 7;
+
+fsc1_opt.M = 1;
+
+Wop1 = wavelet_factory_1d(N, filt1_opt, sc1_opt);
+fWop1 = wavelet_factory_1d(128, ffilt1_opt, fsc1_opt);
+
+scatt_fun1 = @(x)(log_scat(renorm_scat(scat(x,Wop1))));
+fscatt_fun1 = @(x)(func_output(@scat_freq,2,scatt_fun1(x),fWop1));
+feature_fun1 = @(x)(format_scat(fscatt_fun1(x)));
+
+filt2_opt = filt1_opt;
+filt2_opt.Q = [1 1];
+filt2_opt.J = T_to_J(8192*2,filt2_opt);
+
+sc2_opt = sc1_opt;
+
+ffilt2_opt = ffilt1_opt;
+ffilt2_opt.J = 5;
+
+fsc2_opt = fsc1_opt;
+
+Wop2 = wavelet_factory_1d(N, filt2_opt, sc2_opt);
+fWop2 = wavelet_factory_1d(32, ffilt2_opt, fsc2_opt);
+
+scatt_fun2 = @(x)(log_scat(renorm_scat(scat(x,Wop2))));
+fscatt_fun2 = @(x)(func_output(@scat_freq,2,scatt_fun2(x),fWop2));
+feature_fun2 = @(x)(format_scat(fscatt_fun2(x)));
+
+features = {feature_fun1, feature_fun2};
+
+for k = 1:length(features)
+    fprintf('testing feature #%d...',k);
+    tic;
+    sz = size(features{k}(randn(N,1)));
+    aa = toc;
+    fprintf('OK (%.2fs) (size [%d,%d])\n',aa,sz(1),sz(2));
+end
+
+db = prepare_database(src,features);
+db.features = single(db.features);
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 1;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_time_scat_l1.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,32 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% M=1 scattering
+
+preffix = 'time_scat_l1_';
+
+N=5*2^17;
+
+global src;
+
+fparam.filter_type = {'gabor_1d','morlet_1d'};
+fparam.Q = [8 2];
+fparam.J = T_to_J(8192*2,fparam);
+
+options.M = 1;
+
+Wop = wavelet_factory_1d(N, fparam, options);
+
+feature_fun = {@(x)(format_scat(log_scat(renorm_scat(scat(x,Wop)))))};
+
+db = prepare_database(src,feature_fun);
+db.features = single(db.features)
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 1;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_time_scat_l2.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,32 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% M=2 scattering
+
+preffix = 'time_scat_l2_';
+
+N=5*2^17;
+
+global src;
+
+fparam.filter_type = {'gabor_1d','morlet_1d'};
+fparam.Q = [8 2];
+fparam.J = T_to_J(8192*2,fparam);
+
+options.M = 2;
+
+Wop = wavelet_factory_1d(N, fparam, options);
+
+feature_fun = {@(x)(format_scat(log_scat(renorm_scat(scat(x,Wop)))))};
+
+db = prepare_database(src,feature_fun);
+db.features = single(db.features)
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 1;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/experiments/exp_time_scat_l3.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,32 @@
+% Script to reproduce the experiments leading to the results provided in the
+% Table 2 of the paper "Deep Scattering Spectrum" by J. Andén and S. Mallat.
+
+% M=3 scattering
+
+preffix = 'time_scat_l3_';
+
+N=5*2^17;
+
+global src;
+
+fparam.filter_type = {'gabor_1d','morlet_1d','morlet_1d'};
+fparam.Q = [8 2 1];
+fparam.J = T_to_J(8192*2,fparam);
+
+options.M = 3;
+
+Wop = wavelet_factory_1d(N, fparam, options);
+
+feature_fun = {@(x)(format_scat(log_scat(renorm_scat(scat(x,Wop)))))};
+
+db = prepare_database(src,feature_fun);
+db.features = single(db.features)
+db = svm_calc_kernel(db,'gaussian','square',1:8:size(db.features,2));
+
+optt.kernel_type = 'gaussian';
+optt.C = 2.^[0:4:8];
+optt.gamma = 2.^[-16:4:-8];
+optt.search_depth = 3;
+optt.full_test_kernel = 0;
+
+classify(db, optt, preffix);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/folds/createFolds.m	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,56 @@
+function [train,test] = createFolds(type)
+
+% 'none' to use no filtering
+% 'fault' to use filtering
+
+switch type
+  case 'fault'
+    testname = 'test_filtered.txt';
+    trainname = 'train_filtered.txt';
+    validname = 'valid_filtered.txt';
+  case 'none'
+    testname = 'test_stratified.txt';
+    trainname = 'train_stratified.txt';
+    validname = 'valid_stratified.txt';
+end
+
+genrelabels = {'blues','classical','country','disco','hiphop','jazz','metal','pop','reggae','rock'};
+
+% read in test partition
+fid = fopen(testname);
+C = textscan(fid,'%s');
+testdata = zeros(length(C{:}),1);
+for cc=1:length(C{:})
+  genremult = (find(strncmp(C{1}(cc),genrelabels,3))-1)*100;
+  exnum = str2num(C{1}{cc}(end-5:end-4));
+  testdata(cc) = genremult+exnum+1;
+end
+fclose(fid);
+
+% read in valid partition
+fid = fopen(validname);
+C = textscan(fid,'%s');
+validdata = zeros(length(C{:}),1);
+for cc=1:length(C{:})
+  genremult = (find(strncmp(C{1}(cc),genrelabels,3))-1)*100;
+  exnum = str2num(C{1}{cc}(end-5:end-4));
+  validdata(cc) = genremult+exnum+1;
+end
+fclose(fid);
+
+% read in train partition
+fid = fopen(trainname);
+C = textscan(fid,'%s');
+traindata = zeros(length(C{:}),1);
+for cc=1:length(C{:})
+  genremult = (find(strncmp(C{1}(cc),genrelabels,3))-1)*100;
+  exnum = str2num(C{1}{cc}(end-5:end-4));
+  traindata(cc) = genremult+exnum+1;
+end
+fclose(fid);
+
+testbool = zeros(1000,1); testbool(testdata) = 1;
+trainbool = zeros(1000,1); trainbool(validdata) = 1; trainbool(traindata) = 1;
+
+test = logical(testbool);
+train = logical(trainbool);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/folds/test_filtered.txt	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,290 @@
+blues/blues.00012.wav
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dss/folds/test_stratified.txt	Wed Oct 21 00:02:11 2015 +0100
@@ -0,0 +1,250 @@
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