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Baseline classification system. Note that needs ma toolbox (for comoputation of mfccs) and pmtk3 toolbox
author Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk>
date Fri, 31 May 2013 12:25:30 +0100
parents
children
files ComputeFeatures.m Run5FoldClass.m baseline.m classification_folds/fold1_test.txt classification_folds/fold1_train.txt classification_folds/fold2_test.txt classification_folds/fold2_train.txt classification_folds/fold3_test.txt classification_folds/fold3_train.txt classification_folds/fold4_test.txt classification_folds/fold4_train.txt classification_folds/fold5_test.txt classification_folds/fold5_train.txt results/baseline_31052013-11:49.txt results/output_fold1.txt results/output_fold2.txt results/output_fold3.txt results/output_fold4.txt results/output_fold5.txt
diffstat 19 files changed, 824 insertions(+), 0 deletions(-) [+]
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ComputeFeatures.m	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,93 @@
+function [fea, feaNam, feaSiz] = ComputeFeatures(wav, par)
+% Computes a feature vector consisting of MFCCs coefficients and
+% coefficients derived from a matching pursuit decomposition with Gabor
+% atoms.
+%
+% [fea, feaNam] = computeMFCCsAndMP(wav,par)
+%
+% Input
+% -wav: file name locating a .wav audio signal.
+% -par: struct of paramters with the following fields
+%   .fs (22050): sampling frequency
+%   .num_ceps_coeffs (13): number of cepstral coefficients
+%   .mel_filt_bank ([0 11025 23]): extrema and number of mel frequency
+%   bands
+%   .use_first_coeff (false): retain 1st MFCC coefficient
+%   .fft_size (1024): length of fft
+%   .hopsize (512): overlap of consecutive fft
+% Output
+% -fea: matrix of features (one column per frame)
+% -feaNam: struct containing names of features
+%% Unit test
+if ~nargin, [fea, feaNam, feaSiz] = unitTest; return, end
+
+%% Defaults
+if ~exist('par','var') || isempty(par), par = struct; end
+
+def.fs = 22050;                         %sampling rate
+def.fft_size = 1024;                    %size of window
+def.hopsize = 512;                      %step size
+def.usePreEmphasis = false;              %use pre-emphasis (high pass filter)
+def.feaNam = {'mfcc','dmfcc','nme','hos','zcr','sro','scn','sfl','lpc','mpf'};
+
+par = setdefaultoptions(par,def);       %set default options
+
+%% Compute features
+s = preprocessAudio(wav,par);                       %preprocess audio file
+
+feaSiz = [];
+fea = [];
+par.feaNam = {par.feaNam};
+for iFea=1:length(par.feaNam);
+    switch par.feaNam{iFea}
+        case {'mfcc','dmfcc','nme'}             %MFCCs and related
+            [mfcc,~,mel] = ma_mfcc(s,par);
+            switch par.feaNam{iFea}
+                case 'mfcc'
+                    x = mfcc;
+                case 'dmfcc'
+                    x = derivative(mfcc);
+                case 'nme'
+                    x = mel*diag(1./sum(mel)); %energy in each mel band normalized by total energy
+            end
+        case 'hos'                                  %Higher order statistics (see Chi2003Ba)
+            x = (kurtosis(s)/(var(s)^2))*ones(1,fix(length(s)/par.hopsize)-1);
+        case 'zcr'
+            x = zcr(s,par.fft_size,par.hopsize,par.fs)'; %zero crossing rate
+        case 'sro'
+            x = SpectralRollOff(s,par.fft_size,par.hopsize,0.80,par.fs);    %spectral roll-off
+        case 'scn'
+            x = SpectralCentroid(s,par.fft_size,par.hopsize,par.fs)';       %spectral centroid
+        case 'sfl'
+            x = SpectralFlux(s,par.fft_size,par.hopsize,par.fs)';           %spectral flux
+        case 'lpc'
+            x = LPCFeatures(s,par);                             %LPC features
+        case 'mpf'
+            x = GaborFeatures(s,par);                      %Gabor features
+    end
+feaSiz = [feaSiz, size(x,1)];
+fea = [fea; x];
+feaNam = par.feaNam;
+end
+
+function s = preprocessAudio(wav,par)
+s = wavread(wav);                   %read file
+s = s(1:2:end,:);                   %subsample audio (from 44.1kHz tp 22.05kHz)
+if size(s,2)>0, s = mean(s,2); end  %convert to mono
+if par.usePreEmphasis               %apply pre-emphasis filter that highlights high frequencies
+    h = [1, -15/16];                %see Fundamentals of speech processing (Rabiner, Juang)
+    s = filter(h,1,s);
+end
+s = s/max(abs(s));                  %normalize audio
+
+function dmfcc = derivative(mfcc)
+dmfcc = zeros(size(mfcc));          %mfccs 1st derivative
+for iRow=1:size(mfcc,1)
+    temp = conv([mfcc(iRow,1) mfcc(iRow,:) mfcc(iRow,end)],[1/2,0,-1/2],'same');
+    dmfcc(iRow,:) = temp(2:end-1);
+end
+
+function [fea, feaNam, feaSizes] = unitTest
+clear, clc, close all
+file = 'bus01.wav';
+[fea, feaSizes] = ComputeFeatures(file);
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Run5FoldClass.m	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,9 @@
+addpath(genpath('ma'))
+addpath(genpath('pmtk3'))
+addpath(genpath('classification_folds'))
+
+for iFold=1:5
+    baseline(['classification_folds/fold' num2str(iFold) '_train.txt'],...
+        ['classification_folds/fold' num2str(iFold) '_test.txt'],...
+        ['output_fold' num2str(iFold) '.txt']);
+end
\ No newline at end of file
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/baseline.m	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,102 @@
+function tesCat = baseline(train_files,test_files,out_name)
+% Baseline ASC function using mfccs and gmms.
+% Input: 
+%   - train_files: text file containing the path to the training files
+%   - test_files: text file containing the path to the test files
+%   - out_name: text file where to write the results
+% Output:
+%   - est_cat: cell of strings containing the estimated classes of the test
+%   files
+% 
+%% Input parsing and parameters
+% Open files, retrieve paths and training categories.
+trainData = textscan(fopen(train_files,'r'),'%s');
+fclose('all');
+nTraFil   = length(trainData{1})/2;
+traPath    = cell(nTraFil,1);
+traCat    = cell(nTraFil,1);
+for i=1:nTraFil
+    traPath{i} = trainData{1}{2*i-1};
+    traCat{i} = trainData{1}{2*i};
+    
+    traPath{i} = regexprep(traPath{i},'import','Volumes'); 
+end
+
+testData = textscan(fopen(test_files,'r'),'%s');
+fclose('all');
+nTesFil   = length(testData{1});
+tesPath    = cell(nTesFil,1);
+tesCat    = cell(nTesFil,1);
+for i=1:nTesFil
+    tesPath{i} = testData{1}{i};
+    tesPath{i} = regexprep(tesPath{i},'import','Volumes'); 
+end
+
+% Set parameters
+feaNam = {'mfcc'};                                  %compute mfccs
+par = struct('fs', 22050,...                        %sampling frequency
+             'fft_size', 1024,...                   %size of Fourier transform
+             'hopsize', 512,...                     %overlap of Fourier transfrom windows
+             'usePreEmphasis', false,...            %do not filter sound prior to feature extraction
+             'feaNam',feaNam,...                    %extract mfcc coefficients
+             'nGaussCom',5,...                      %use 5 Gaussian mixture components
+             'empar', struct('nrandomRestarts',10,...%restart algoritms 5 times and pick the best model
+                             'maxIter',50,...       %maximum number of iterations
+                             'verbose',false));
+
+%% Training phase
+% Extract features from each training signal
+traFea = cell(nTraFil,1);
+for iFil=1:length(traPath)
+    fprintf('\n Extracting %s features from file %s - (%s)...',feaNam{:},traPath{iFil},traCat{iFil});
+    traFea{iFil} = ComputeFeatures(traPath{iFil},par);
+    fprintf(' done!');
+end
+
+% Pre-process features by subtracting mean and dividing by standard
+% deviation
+muFea = mean([traFea{:}],2);
+sdFea    = std([traFea{:}],[],2);
+for iFil=1:nTraFil
+    traFea{iFil} = (traFea{iFil}-repmat(muFea,1,size(traFea{iFil},2)))./repmat(sdFea,1,size(traFea{iFil},2));
+end
+
+% Compute statistical distribution of the featuers belonging to a given
+% category using GMMs
+staMod = 'GMMs';                %statistical model
+uniCat = unique(traCat);        %unique categories
+nUniCat = length(uniCat);
+models = cell(nUniCat,1);
+for iUniCat=1:nUniCat
+    fprintf('\n Computing statistical distribution of features from class %s using %s ...',uniCat{iUniCat},staMod);
+    idx = strcmpi(traCat,uniCat(iUniCat));    %indexes of trainig files belonging to iUniCat category
+    traFeaMat = [traFea{idx}]';
+    models{iUniCat} = mixGaussFit(traFeaMat,par.nGaussCom,par.empar);
+    fprintf(' done!');
+end
+
+
+%% Test phase
+% Repeat processing chain and compute the features extracted from test files
+for iFil=1:nTesFil
+    fprintf('\n Extracting %s features from file %s...',feaNam{:},tesPath{iFil});
+    tesFea = ComputeFeatures(tesPath{iFil},par);
+    % pre-process features using the mean and standard deviation values
+    % computed on the training set
+    tesFea = (tesFea-repmat(muFea,1,size(tesFea,2)))./repmat(sdFea,1,size(tesFea,2));
+    fprintf(' done!');
+    % Compute likelihood scores 
+    scores = nan(length(models),1);
+    for iModel=1:length(models)
+        scores(iModel) = sum(mixGaussLogprob(models{iModel},tesFea'));   %returns the negative loglikelihood of the data given the model iModel
+    end
+    [~, ind] = max(scores);
+    tesCat{iFil} = uniCat{ind(1)};
+end
+filNam = [mfilename,'_',datestr(now,'ddmmyyyy-HH:MM'),'.txt'];
+fId = fopen(['results', filesep, out_name],'w');
+for iFil=1:nTesFil
+    fprintf(fId,[tesPath{iFil} '\t']);
+    fprintf(fId,[tesCat{iFil} '\n']);
+end
+fclose(fId);
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/classification_folds/fold1_test.txt	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,20 @@
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_phjfbc.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_aberpx.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_pfofsn.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_ujseca.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_isiztb.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_irqnfm.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_rsvlmu.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_unaini.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_vlobhu.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_rocbnk.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_xgyhay.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_hxrrqc.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_njvbds.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_rqypxr.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_phsljf.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_fgwawe.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_kaujfj.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_tdaact.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/1_xxntjg.wav
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/6_sqsejp.wav
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/classification_folds/fold1_train.txt	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,80 @@
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/2_jcptzg.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/3_noamdk.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/4_whkkcj.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/5_kkhcby.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/7_ramkwa.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/8_xqomyo.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/9_zdpnzy.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_tojtld.wav	bus
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/2_goevrg.wav	busystreet
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/3_nuxbkj.wav	busystreet
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/9_mxjjef.wav	busystreet
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_oinmps.wav	openairmarket
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_gfvrsk.wav	park
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/2_ukfzgr.wav	quietstreet
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/3_yincfh.wav	quietstreet
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/4_viopuy.wav	quietstreet
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_xquups.wav	restaurant
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/4_nczycf.wav	supermarket
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/3_hrvynb.wav	tube
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+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/9_fnupsk.wav	tube
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_uhvyhh.wav	tube
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/2_slpvce.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/3_egqsuw.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/4_vrfvwj.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/5_aonzqh.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/7_tfqidq.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/8_fkztav.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/9_jwuonk.wav	tubestation
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/1_mnkevj/10_tewtyk.wav	tubestation
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/classification_folds/fold2_test.txt	Fri May 31 12:25:30 2013 +0100
@@ -0,0 +1,20 @@
+/import/c4dm-datasets/aasp_challenge/eval2013/sc_folds/2_aromtw/2_wmmhuo.wav
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/baseline_31052013-11:49.txt	Fri May 31 12:25:30 2013 +0100
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/output_fold1.txt	Fri May 31 12:25:30 2013 +0100
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/output_fold2.txt	Fri May 31 12:25:30 2013 +0100
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/output_fold3.txt	Fri May 31 12:25:30 2013 +0100
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/output_fold4.txt	Fri May 31 12:25:30 2013 +0100
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/results/output_fold5.txt	Fri May 31 12:25:30 2013 +0100
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