Part-2 Tutorial on Singing Transcription (by Roger Jang)

Contents

This is the part-3 tutorial on singing transcription from polyphonic music, for AI Cup Competition. This part will focus on evaluation on singing transcription. You should have read part-1 tutorial and part-2 tutorial before reading this part.

Preprocessing

Before we start, let's add necessary toolboxes to the search path of MATLAB:

addpath d:/users/jang/matlab/toolbox/utility
addpath d:/users/jang/matlab/toolbox/sap
addpath d:/users/jang/matlab/toolbox/machineLearning

All the above toolboxes can be downloaded from Roger's toolbox page. Make sure you are using the latest toolboxes to work with this script.

For compatibility, here we list the platform and MATLAB version that we used to run this script:

fprintf('Platform: %s\n', computer);
fprintf('MATLAB version: %s\n', version);
fprintf('Date & time: %s\n', char(datetime));
scriptStartTime=tic;	% Timing for the whole script

Platform: PCWIN64
MATLAB version: 9.6.0.1214997 (R2019a) Update 6
Date & time: 19-Jun-2020 23:22:29

ST evaluation of a song

Here we want to focus on the evaluation of singing transcription on a single song. We have put every component for doing so in a file "goTestOne.m", as follows:

type goTestOne.m

feaFile='d:\dataSet\public\MIR-ST500\6\6_feature.json';
fprintf('Reading %s...', feaFile);
myTic=tic; fea=jsondecode(fileread(feaFile)); fprintf('==> Time=%g sec\n', toc(myTic));
pv.pitch=fea.vocal_pitch;
pv.time=fea.time;
pv.name='隱形的翅膀';
%opt=pvPlot('defaultOpt'); opt.showPlayButton=0;
%figure; pvPlot(pv, opt);
%%
gtFile='d:\dataSet\public\MIR-ST500\6\6_groundtruth.txt';
fprintf('Reading %s...', gtFile);
myTic=tic; noteGt=noteFileRead(gtFile); fprintf('==> Time=%g sec\n', toc(myTic));
%%
fprintf('Performing pv2note...');
opt=pv2note('defaultOpt');
opt.method='simple00';
%opt.gtNote=noteGt;
myTic=tic; notePredicted=pv2note(pv, opt); fprintf('==> Time=%g sec\n', toc(myTic));
fprintf('No. of GT notes=%d\n', length(noteGt.pitch));
fprintf('No. of predicted notes=%d\n', length(notePredicted.pitch));
%%
fprintf('Performing noteVecSim...');
myTic=tic; fMeasure=noteVecSim(notePredicted, noteGt);  fprintf('==> Time=%g sec\n', toc(myTic));
fprintf('fMeasure=%g\n', fMeasure);

We can run the above script to see how it goes:

goTestOne

Reading d:\dataSet\public\MIR-ST500\6\6_feature.json...==> Time=0.123689 sec
Reading d:\dataSet\public\MIR-ST500\6\6_groundtruth.txt...==> Time=0.0011969 sec
Performing pv2note...==> Time=0.0058614 sec
No. of GT notes=245
No. of predicted notes=136
Performing noteVecSim...==> Time=0.0038839 sec
fMeasure=0.259318

ST evaluation of MIR-ST500 dataset

Then we can perform the evaluation over all songs in MIR-ST500 dataset, as follows:

% Get all groundtruth files
melody=dir('d:/dataSet/public/MIR-ST500/*/*_groundtruth.txt');
melodyCount=length(melody);
fprintf('melodyCount=%d\n', melodyCount);
% Get song ID
for i=1:melodyCount
	items=split(melody(i).name, '_');
	melody(i).songId=eval(items{1});
end
% Ordered by songID
songId=[melody.songId];
[sorted, index]=sort(songId);
melody=melody(index);
% Read each feature file and perform singing transcription
myTic=tic;
for i=1:melodyCount
	gtFilePath=fullfile(melody(i).folder, melody(i).name);
	% Read the groundtruth
	melody(i).noteGt=noteFileRead(gtFilePath);
	% Read the feature file
	feaFilePath=fullfile(melody(i).folder, sprintf('%d_feature.json', melody(i).songId));
	fea=jsondecode(fileread(feaFilePath));
	pv.pitch=fea.vocal_pitch;
	pv.time=fea.time;
	melody(i).notePredicted=pv2note(pv, opt);
	melody(i).fMeasure=noteVecSim(melody(i).notePredicted, melody(i).noteGt);
	fprintf('%d/%d: file=%s, f-measure=%g\n', i, melodyCount, gtFilePath, melody(i).fMeasure);
end
fprintf('Time for pv2note() and noteVecSim(): %g sec\n', toc(myTic));
fprintf('Overall f-measure=%g\n', mean([melody.fMeasure]));

melodyCount=500
1/500: file=d:\dataSet\public\MIR-ST500\1\1_groundtruth.txt, f-measure=0.35763
2/500: file=d:\dataSet\public\MIR-ST500\2\2_groundtruth.txt, f-measure=0.326394
3/500: file=d:\dataSet\public\MIR-ST500\3\3_groundtruth.txt, f-measure=0.217391
4/500: file=d:\dataSet\public\MIR-ST500\4\4_groundtruth.txt, f-measure=0.290541
5/500: file=d:\dataSet\public\MIR-ST500\5\5_groundtruth.txt, f-measure=0.315479
6/500: file=d:\dataSet\public\MIR-ST500\6\6_groundtruth.txt, f-measure=0.259318
7/500: file=d:\dataSet\public\MIR-ST500\7\7_groundtruth.txt, f-measure=0.265432
8/500: file=d:\dataSet\public\MIR-ST500\8\8_groundtruth.txt, f-measure=0.25034
9/500: file=d:\dataSet\public\MIR-ST500\9\9_groundtruth.txt, f-measure=0.220551
10/500: file=d:\dataSet\public\MIR-ST500\10\10_groundtruth.txt, f-measure=0.277904
11/500: file=d:\dataSet\public\MIR-ST500\11\11_groundtruth.txt, f-measure=0.331302
12/500: file=d:\dataSet\public\MIR-ST500\12\12_groundtruth.txt, f-measure=0.246792
	Warning: many-to-1 mapping, p=[2;4;5;6;10;10;11;12;13;16;18;19;21;22;23;26;29;30;35;36;37;38;39;40;42;44;46;47;49;50;51;53;54;55;57;58;59;60;61;62;63;65;66;70;73;74;75;76;78;79;80;81;83;90;91;92;93;94;97;99;104;107;109;110;116;117;119;120;122;123;124;125;127;128;129;131;133;134;135;138;139;141;142;143;145;147;149;150;154;157;158;160;161;162;163]
13/500: file=d:\dataSet\public\MIR-ST500\13\13_groundtruth.txt, f-measure=0.20284
14/500: file=d:\dataSet\public\MIR-ST500\14\14_groundtruth.txt, f-measure=0.195407
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19/500: file=d:\dataSet\public\MIR-ST500\19\19_groundtruth.txt, f-measure=0.207481
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25/500: file=d:\dataSet\public\MIR-ST500\25\25_groundtruth.txt, f-measure=0.109202
	Warning: many-to-1 mapping, p=[1;2;3;4;6;8;9;10;12;13;14;20;21;24;27;28;29;30;32;33;34;37;38;39;43;46;47;48;49;50;51;54;57;58;62;63;64;69;70;71;72;73;76;77;80;81;82;83;83;86;88;91;92;93;94;95;96;97;100;101;102;103;105;106;108;111;113;114;115;117;118;119;120;121;123;125;126;127;128;132;133;134;135;136]
26/500: file=d:\dataSet\public\MIR-ST500\26\26_groundtruth.txt, f-measure=0.143219
27/500: file=d:\dataSet\public\MIR-ST500\27\27_groundtruth.txt, f-measure=0.270896
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66/500: file=d:\dataSet\public\MIR-ST500\66\66_groundtruth.txt, f-measure=0.200782
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68/500: file=d:\dataSet\public\MIR-ST500\68\68_groundtruth.txt, f-measure=0.423246
69/500: file=d:\dataSet\public\MIR-ST500\69\69_groundtruth.txt, f-measure=0.226855
	Warning: many-to-1 mapping, p=[3;4;5;6;7;10;12;13;14;16;22;23;24;26;27;28;29;31;32;33;34;35;36;37;38;39;40;42;43;44;45;46;47;48;50;51;53;54;55;56;57;58;59;60;61;63;65;67;68;69;70;72;73;74;75;77;78;79;80;81;84;86;87;90;91;93;95;96;98;99;100;102;103;104;105;106;107;108;110;111;112;113;114;115;116;117;118;119;120;123;124;125;126;127;128;129;130;131;133;134;136;137;138;139;140;141;143;146;147;148;148;150;151;156;157;158;159;160;161;163;164;168;171;175;177;178;179;181;182;183;184;186;187;189;190;191;198;199;201;202;203;204;205;207;208;209;211;212;214;216;222;223]
70/500: file=d:\dataSet\public\MIR-ST500\70\70_groundtruth.txt, f-measure=0.199433
71/500: file=d:\dataSet\public\MIR-ST500\71\71_groundtruth.txt, f-measure=0.189147
72/500: file=d:\dataSet\public\MIR-ST500\72\72_groundtruth.txt, f-measure=0.242424
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133/500: file=d:\dataSet\public\MIR-ST500\133\133_groundtruth.txt, f-measure=0.224044
	Warning: many-to-1 mapping, p=[1;2;3;4;6;8;9;10;11;12;13;13;15;16;17;17;18;20;21;23;24;25;25;29;30;31;33;35;37;38;39;40;41;42;43;44;45;46;47;49;50;51;53;55;56;57;59;60;61;62;63;64;65;66;67;68;69;71;72;74;75;76;77;78;80;81;82;83;84;85;86;87;88;92;93;94;96;99;102;106;108;109;110;111;112;113;114;115;117;119;120;121;122;123;124;126;127;128;129;130;131;134]
134/500: file=d:\dataSet\public\MIR-ST500\134\134_groundtruth.txt, f-measure=0.382785
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	Warning: many-to-1 mapping, p=[6;7;8;9;10;12;13;14;15;17;19;20;21;22;26;27;28;29;30;31;33;35;36;39;40;41;42;43;45;46;47;48;49;50;51;52;54;56;61;62;64;66;67;68;72;73;75;76;77;78;80;82;83;84;85;86;88;92;93;94;94;95;98;99;100;101;102;103;104;107;108;109;110;111;115;116;120;121;122;123;124;125;127;129;131;132;133;135;137;138;140;141;147;148;151;153;156;158;159;160;161;163;164;165;166;167;168;169;172;174;175;176;177;178;179;181;183;184;186;187;188;189;190;191;193]
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Time for pv2note() and noteVecSim(): 71.2521 sec
Overall f-measure=0.27856

Since the above procedure will be invoked many times for iterative performance optimization, we shall use several function to finish the above task. First of all, let's read the dataset all at once:

dsDir='d:/dataSet/public/MIR-ST500';
[melody, time]=stDsRead(dsDir);		% Read the dataset
fprintf('Time for reading "%s" = %g sec\n', dsDir, time);

Time for reading "d:/dataSet/public/MIR-ST500" = 72.7803 sec

Then we can try to do note detection:

opt=stDsPredict('defaultOpt');
[melody02, time]=stDsPredict(melody, opt, 1);	% Evaluate the performance
fprintf('Elapsed time=%g sec.\n', time);

1/500: gtFile=d:\dataSet\public\MIR-ST500\1\1_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\1\1_feature.json
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472/500: gtFile=d:\dataSet\public\MIR-ST500\472\472_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\472\472_feature.json
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475/500: gtFile=d:\dataSet\public\MIR-ST500\475\475_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\475\475_feature.json
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477/500: gtFile=d:\dataSet\public\MIR-ST500\477\477_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\477\477_feature.json
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481/500: gtFile=d:\dataSet\public\MIR-ST500\481\481_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\481\481_feature.json
482/500: gtFile=d:\dataSet\public\MIR-ST500\482\482_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\482\482_feature.json
483/500: gtFile=d:\dataSet\public\MIR-ST500\483\483_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\483\483_feature.json
484/500: gtFile=d:\dataSet\public\MIR-ST500\484\484_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\484\484_feature.json
485/500: gtFile=d:\dataSet\public\MIR-ST500\485\485_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\485\485_feature.json
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491/500: gtFile=d:\dataSet\public\MIR-ST500\491\491_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\491\491_feature.json
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493/500: gtFile=d:\dataSet\public\MIR-ST500\493\493_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\493\493_feature.json
494/500: gtFile=d:\dataSet\public\MIR-ST500\494\494_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\494\494_feature.json
495/500: gtFile=d:\dataSet\public\MIR-ST500\495\495_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\495\495_feature.json
496/500: gtFile=d:\dataSet\public\MIR-ST500\496\496_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\496\496_feature.json
497/500: gtFile=d:\dataSet\public\MIR-ST500\497\497_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\497\497_feature.json
498/500: gtFile=d:\dataSet\public\MIR-ST500\498\498_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\498\498_feature.json
499/500: gtFile=d:\dataSet\public\MIR-ST500\499\499_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\499\499_feature.json
500/500: gtFile=d:\dataSet\public\MIR-ST500\500\500_groundtruth.txt, feaFile=d:\dataSet\public\MIR-ST500\500\500_feature.json
Elapsed time=97.4718 sec.

Then we can start performance evaluation:

opt=stDsPerfEval('defaultOpt');
[perf, time]=stDsPerfEval(melody02, opt, 1);	% Evaluate the performance
fprintf('Average f-measure=%g, elapsed time=%g sec.\n', perf, time);

	Warning: many-to-1 mapping, p=[2;4;5;6;10;10;12;13;14;15;18;20;21;23;24;26;29;32;33;34;35;40;41;42;43;44;45;47;48;50;52;53;54;56;57;58;59;60;62;63;64;66;71;72;73;74;75;76;77;78;79;80;83;84;85;86;92;95;96;97;99;101;102;104;105;106;107;109;116;117;118;119;120;121;123;124;127;129;134;137;138;140;141;142;148;149;151;152;154;155;156;158;160;162;163;164;166;168;170;171;172;173;176;179;180;182;183;184;185;186;188;189;190;191;193;194;195;197;198;199;200;203;205;208;209;212;213;214;215;216;217]
	Warning: many-to-1 mapping, p=[1;4;6;7;9;12;13;14;15;17;18;20;21;22;26;32;33;34;37;40;41;42;43;44;45;46;47;48;50;51;52;54;56;57;58;64;65;67;68;69;72;73;74;75;77;78;82;83;84;88;89;90;91;95;96;97;98;103;104;107;109;111;114;115;116;120;122;123;125;127;127;131;134;135;138;139;140;142;143;144;145;148;149;150;151;154;155;157;158;160;163;165;166;167;169;170;171;172;173;174;175;178;180;181;183;184;185;189;190;191;192;193;194;195]
	Warning: many-to-1 mapping, p=[3;4;5;6;7;11;16;19;20;22;30;31;32;35;36;37;38;39;40;42;43;44;45;46;47;48;50;52;53;54;55;57;58;59;60;61;63;65;67;68;70;71;72;73;74;75;76;77;79;81;83;85;86;87;88;90;92;93;94;95;97;98;99;101;102;105;110;113;116;117;118;120;121;123;124;127;129;130;131;132;134;135;136;137;138;139;140;141;142;143;144;145;146;149;150;151;153;154;155;157;161;162;163;164;165;166;167;168;169;170;172;173;176;177;178;179;181;182;183;187;188;189;190;191;192;192;194;195;196;200;202;204;205;206;207;208;209;212;213;214;218;222;228;231;232;233;235;236;237;238;241;243;246;247;248;256;257;259;261;262;263;264;265;266;268;269;270;271;276;277;279;280;282;290;291;292]
	Warning: many-to-1 mapping, p=[1;2;3;4;6;9;10;11;12;13;14;15;16;16;18;19;20;20;21;22;25;26;29;30;32;32;37;38;39;41;42;45;47;49;50;51;52;53;54;55;56;57;58;59;60;62;64;65;67;69;72;74;75;76;78;79;81;82;84;85;86;87;88;89;90;93;94;95;98;99;101;102;103;106;107;108;110;111;112;114;115;117;121;122;124;127;131;134;138;141;143;144;145;146;147;148;149;150;151;153;155;158;159;160;161;162;163;164;166;168;171;172;173;174;175;179]
	Warning: many-to-1 mapping, p=[3;4;5;7;9;11;13;14;15;16;17;18;19;20;22;23;24;24;27;28;29;30;31;32;35;36;37;38;39;41;42;43;44;45;46;47;48;49;50;51;52;54;55;57;58;59;61;63;64;65;66;67;70;72;75;77;80;82;84;86;90;91;93;94;97;100;101;103;104;105;106;107;110;111;113;114;115;122;123;124;126;127;129;132;133;134;135;136;138;139;140;141;142;144;146;148;150;152;153;154;155;156;157;158;160;163;164;166;170;172;173;174;177;178;179;181;182;183;184;186;190;192;193;194;195;196;198;199;200;201;202;204;205;207;209;210;211;214;216;217;218;220;223;228;230;233;235;237;239;240;241;242;243;244;246;247;248;249;250;251;254;257;259;263;264;265;266;267;268;269;270;271]
	Warning: many-to-1 mapping, p=[6;7;8;9;10;13;14;15;16;18;20;21;22;23;28;29;30;31;32;33;35;37;38;39;42;43;44;45;46;47;49;50;51;52;53;54;55;56;57;59;61;67;68;69;70;73;74;76;78;79;80;81;87;88;89;90;92;94;95;96;97;100;101;104;105;106;107;108;109;111;115;116;117;117;118;122;123;124;125;126;127;128;132;133;134;135;137;138;143;144;145;150;151;152;153;155;157;159;161;164;165;166;167;169;170;172;173;175;182;183;184;186;190;193;195;196;198;201;203;204;205;208;211;213;214;215;216;219;220;223;224;226;227;228;229;230;234;236;237;240;241;242;244;245;247;249;252;253;255;256;257;258;259;260;262]
Average f-measure=0.329787, elapsed time=1.79791 sec.

Summary

This is a brief tutorial on singing transcription. There are several directions for further improvement:

Appendix

List of functions, scripts, and datasets used in this script:

Date and time when finishing this script:

fprintf('Date & time: %s\n', char(datetime));

Date & time: 19-Jun-2020 23:26:35

Overall elapsed time:

toc(scriptStartTime)

Elapsed time is 245.388673 seconds.

Jyh-Shing Roger Jang, created on

datetime

ans = 

  datetime

   19-Jun-2020 23:26:35

If you are interested in the original MATLAB code for this page, you can type "grabcode(URL)" under MATLAB, where URL is the web address of this page.

Published with MATLAB® R2019a