Difference between revisions of "2009:Query-by-Singing/Humming Results"

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==Introduction==
 
==Introduction==
−
These are the results for the 2008 running of the Query-by-Singing/Humming task. For background information about this task set please refer to the [[Query by Singing/Humming]] page.  
+
These are the results for the 2008 running of the Query-by-Singing/Humming task. For background information about this task set please refer to the [[2009:Query by Singing/Humming]] page.  
  
 
===Task Descriptions===
 
===Task Descriptions===
Line 6: Line 6:
 
'''Task 1 [[#Task 1 Results|Goto Task 1 Results]]''': The first subtask is the same as last year. In this subtask, submitted systems take a sung query as input and return a list of songs from the test database. Mean reciprocal rank (MRR) of the ground truth, as well as the simple hit(1)/miss(0) counting, is calculated over the top 10 returns. Two data sets are used:
 
'''Task 1 [[#Task 1 Results|Goto Task 1 Results]]''': The first subtask is the same as last year. In this subtask, submitted systems take a sung query as input and return a list of songs from the test database. Mean reciprocal rank (MRR) of the ground truth, as well as the simple hit(1)/miss(0) counting, is calculated over the top 10 returns. Two data sets are used:
  
−
*[[#Task 1, Jang's dataset Results]] Roger Jang's [http://mirlab.org/dataSet/public/MIR-QBSH-corpus.rar MIR-QBSH corpus] with 48 songs as ground truth + 2000 Essen Collection MIDI noise files. See [http://www.esac-data.org/  ESAC Data Homepage] for more information about the Essen Collection. The queries consists of 4431 humming. All queries are from the beginning of references  
+
*[[#Task 1a, Jang's dataset Results|Jang's Dataset Results]] Roger Jang's [http://mirlab.org/dataSet/public/MIR-QBSH-corpus.rar MIR-QBSH corpus] with 48 songs as ground truth + 2000 Essen Collection MIDI noise files. See [http://www.esac-data.org/  ESAC Data Homepage] for more information about the Essen Collection. The queries consists of 4431 humming. All queries are from the beginning of references  
−
*[[#Task 1, ThinkIT's dataset Results]] [http://mirlab.org/dataSet/public/IOACAS_QBH_Coprus1.rar IOACAS corpus 1] data set with 106 songs as ground truth + 2000 Essen Collection MIDI noise files. See [http://www.esac-data.org/  ESAC Data Homepage] for more information about the Essen Collection. The queries consists of 355 humming. There are no "singing from beginning" gurantee.
+
*[[#Task 1b, ThinkIT's dataset Results|ThinkIT's Dataset Results]] [http://mirlab.org/dataSet/public/IOACAS_QBH_Coprus1.rar IOACAS corpus 1] data set with 106 songs as ground truth + 2000 Essen Collection MIDI noise files. See [http://www.esac-data.org/  ESAC Data Homepage] for more information about the Essen Collection. The queries consists of 355 humming. There are no "singing from beginning" gurantee.
 +
*[[#Task 1c, IOACAS2's dataset Results|IOACAS's 2nd Dataset Results]] [http://mirlab.org/dataSet/public/IOACAS_QBH_Coprus2.rar IOACAS corpus 2] data set with 192 songs as ground truth + 2000 Essen Collection MIDI noise files. See [http://www.esac-data.org/  ESAC Data Homepage] for more information about the Essen Collection. The queries consists of 404 humming. There are no "singing from beginning" gurantee.
  
−
 
+
'''Task 2 [[#Task 2 Results|Goto Task 2 Results]]''': The second subtask is the query against other humming. In the second subtask, Roger Jang's [http://mirlab.org/dataSet/public/MIR-QBSH-corpus.rar MIR-QBSH corpus] has been divided into two groups (2040 as queries and 2391 as database). The query is performed against the other humming database and the top 10 closed are returned. The score is simple count of how many returns belong to the same ground truth song.
−
'''Task 2 [[#Task 2 Results|Goto Task 2 Results]]''': The second subtask is the query against other humming. In the second subtask, Roger Jang's [http://mirlab.org/dataSet/public/MIR-QBSH-corpus.rar MIR-QBSH corpus] has been divided into two groups (2040 as queries and 2391 as database). The query is performed against the other humming database and the top 10 closed are returned. The score is simple count of how many returns belong to the same ground truth song.  
 
  
 
===General Legend===
 
===General Legend===
 
====Team ID====
 
====Team ID====
−
'''CSJ1''' = [[Chun-Ta Chen and Jyh-Shing Roger Jang]] matched by beginning of the sond
+
'''CSJ1''' = [https://www.music-ir.org/mirex/results/2009/qbt/QbshJang.pdf Jyh-Shing Roger Jang] matched by beginning of the sond<br />
 +
'''CSJ2''' = [https://www.music-ir.org/mirex/results/2009/qbt/QbshJang.pdf Jyh-Shing Roger Jang] matched by anywhere of the song<br />
 +
'''HAFR''' = [https://www.music-ir.org/mirex/results/2009/qbt/HAFR.pdf Pierre Hanna, Julien Allali, Pascal Ferraro,Matthias Robine]<br />
 +
 
 +
===Task 1 Results===
 +
 
 +
 
 +
====Task 1a, Jang's dataset Results====
 +
 
 +
=====Task 1a Overall Results=====
 +
<csv>2009/qbsh/QbshFinalTask1a.csv</csv>
  
−
'''CSJ2''' = [[Chun-Ta Chen and Jyh-Shing Roger Jang]] matched by anywhere of the song
+
=====Task 1a Friedman's Test for Significant Differences=====
 +
The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups.
 +
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);
  
−
'''HAFR''' = [[Pierre Hanna, Julien Allali, Pascal Ferraro and Matthias Robine]]
+
Simple Hit/Miss Count:
 +
<csv>2009/qbsh/friedman/Qbsh1aTask2Simple_friedman_tukeyKramerHSD.csv</csv>
  
 +
[[Image:2009_sqbsh1atask2simple_friedman_mean_ranks.pngγçä]]
  
−
===Task 1 Results===
+
MRR Method:
 +
<csv>2009/qbsh/friedman/Qbsh1aTask2Mrr_friedman_tukeyKramerHSD.csv</csv>
 +
 
 +
[[Image:2009_sqbsh1atask2mrr_friedman_mean_ranks.pngγçä]]
 +
 
 +
=====Task 1a Summary Results by Query Group=====
 +
Simple Hit/Miss Count
 +
<csv>2009/qbsh/QbshTask1aSimpleByGroup.csv</csv>
 +
 
 +
MRR Method
 +
<csv>2009/qbsh/QbshTask1aMrrByGroup.csv</csv>
 +
 
 +
====Task 1a Summary Results by Query ====
 +
Simple Hit/Miss Counting
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1aSimpleByQuery.csv]
  
 +
MRR Method
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1aMrrByQuery.csv]
  
−
===Task 1, Jang's dataset Results===
+
====Task 1b, ThinkIT's dataset Results====
  
−
=====Task 1 Overall Results=====
+
=====Task 1b Overall Results=====
−
<csv>qbsh/qbsh_task1_summary.csv</csv>
+
<csv>2009/qbsh/QbshFinalTask1b.csv</csv>
  
−
====Task 1 Friedman's Test for Significant Differences====
+
=====Task 1b Friedman's Test for Significant Differences=====
 
The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups.
 
The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups.
 
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);
 
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);
−
<csv>qbsh/qbsh.task1.friedman_detailed.csv</csv>
 
  
−
[[Image:Qbsh.task1.friedman.small.png]]
+
Simple Hit/Miss Count:
 +
<csv>2009/qbsh/friedman/Qbsh1bTask2Simple_friedman_tukeyKramerHSD.csv</csv>
  
−
====Task 1 Summary Results by Query Group====
+
[[Image:2009_sqbsh1btask2simple_friedman_mean_ranks.pngγçä]]
−
<csv>qbsh/qbsh.task1.res.byQueryGroup.csv</csv>
 
  
 +
MRR Method:
 +
<csv>2009/qbsh/friedman/Qbsh1bTask2Mrr_friedman_tukeyKramerHSD.csv</csv>
  
−
===Task 1, ThinkIT's dataset Results===
+
[[Image:2009_sqbsh1btask2mrr_friedman_mean_ranks.pngγçä]]
 +
=====Task 1b Summary Results by Query Group=====
 +
Simple Hit/Miss Count
 +
<csv>2009/qbsh/QbshTask1bSimpleByGroup.csv</csv>
  
−
===Task 2 Results===
+
MRR Method
−
In this subtask, the same setup as the first subtask used with combination of different transcribers and matchers. The test databases consists of 106 ground-truth MIDIS + 2000 Essen Collection MIDI noise files. The query databases consists of 355 sung queries.
+
<csv>2009/qbsh/QbshTask1bMrrByGroup.csv</csv>
 +
 
 +
====Task 1b Summary Results by Query ====
 +
Simple Hit/Miss Counting
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1bSimpleByQuery.csv]
 +
 
 +
MRR Method
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1bMrrByQuery.csv]
  
−
=====Task 2 Overall Results=====
+
====Task 1c, IOACAS2's dataset Results====
−
<csv>qbsh/qbsh.task2.summary.csv</csv>
+
As argued by Jang, there are some abnormal in the IOACAS2 data set. Therefore, we just provide the raw result without any further analysis.
  
−
====Task 2 Friedman's Test for Significant Differences====
 
−
The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups.
 
−
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);
 
−
<csv>qbsh/qbsh.task2.friedman_detailed.csv</csv>
 
  
−
[[Image:Qbsh.task2.friedman.s.png]]
+
=====Task 1c Summary Results by Query Group=====
 +
Simple Hit/Miss Count
 +
<csv>2009/qbsh/QbshTask1cSimpleByGroup.csv</csv>
  
−
====Task 2 Summary Results by Query Group====
+
MRR Method
−
<csv>qbsh/qbsh_task2_res_byQueryGroup.csv</csv>
+
<csv>2009/qbsh/QbshTask1cMrrByGroup.csv</csv>
  
−
===Runtime Results===
+
====Task 1c Summary Results by Query ====
 +
Simple Hit/Miss Counting
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1cSimpleByQuery.csv]
  
−
<csv>qbsh.runtime.csv</csv>
+
MRR Method
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask1cMrrByQuery.csv]
  
−
[[Category: Results]]
+
===Task 2 Results===
  
 +
=====Task 2 Overall Results=====
 +
<csv>2009/qbsh/QbshFinalTask2.csv</csv>
  
−
==Introduction==
+
=====Task 2 Friedman's Test for Significant Differences=====
 +
The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups.
 +
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);
  
 +
<csv>2009/qbsh/friedman/QbshTask2_friedman_tukeyKramerHSD.csv</csv>
  
−
===Task Descriptions===
+
[[Image:2009_sqbshtask2_friedman_mean_ranks.png γçä]]
  
−
===General Legend===
 
−
===Team ID===
 
  
−
'''CSJ''' = [[Chun-Ta Chen and Jyh-Shing Roger Jang]]<br />
+
=====Task 2 Summary Results by Query Group=====
−
'''HAFR1''' = [[Pierre Hanna, Julien Allali, Pascal Ferraro and Matthias Robine]]<br />
+
<csv>2009/qbsh/QbshTask2Group.csv</csv>
−
'''HAFR2''' = [[Pierre Hanna, Julien Allali, Pascal Ferraro and Matthias Robine]]<br />
 
−
'''HL''' = [[Shu-Jen Show and Hsiao Tyne Liang]]<br />
 
  
 +
====Task 2 Summary Results by Query ====
 +
[https://www.music-ir.org/mirex/results/2009/qbsh/QbshTask2Query.csv]
  
−
====Jang Collection ====
+
===Runtime Results===
  
−
====Think IT====
+
<csv>2009/qbsh/qbshRunTime.csv</csv>
  
−
====Queries as Variation====
+
[[Category: Results]]

Latest revision as of 21:42, 13 May 2010

Introduction

These are the results for the 2008 running of the Query-by-Singing/Humming task. For background information about this task set please refer to the 2009:Query by Singing/Humming page.

Task Descriptions

Task 1 Goto Task 1 Results: The first subtask is the same as last year. In this subtask, submitted systems take a sung query as input and return a list of songs from the test database. Mean reciprocal rank (MRR) of the ground truth, as well as the simple hit(1)/miss(0) counting, is calculated over the top 10 returns. Two data sets are used:

Task 2 Goto Task 2 Results: The second subtask is the query against other humming. In the second subtask, Roger Jang's MIR-QBSH corpus has been divided into two groups (2040 as queries and 2391 as database). The query is performed against the other humming database and the top 10 closed are returned. The score is simple count of how many returns belong to the same ground truth song.

General Legend

Team ID

CSJ1 = Jyh-Shing Roger Jang matched by beginning of the sond
CSJ2 = Jyh-Shing Roger Jang matched by anywhere of the song
HAFR = Pierre Hanna, Julien Allali, Pascal Ferraro,Matthias Robine

Task 1 Results

Task 1a, Jang's dataset Results

Task 1a Overall Results
CSJ1 CSJ2 HAFR
Simple Count 0.94 0.9 0.77
MRR 0.91 0.86 0.66
Total count 4431 4431 4409

download these results as csv

Task 1a Friedman's Test for Significant Differences

The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups. Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);

Simple Hit/Miss Count:

TeamID TeamID Lowerbound Mean Upperbound Significance
CSJ1 CSJ2 0.3232 0.7812 1.2393 TRUE
CSJ1 HAFR 1.1670 1.6250 2.0830 TRUE
CSJ2 HAFR 0.3857 0.8438 1.3018 TRUE

download these results as csv

File:2009 sqbsh1atask2simple friedman mean ranks.pngγçä

MRR Method:

TeamID TeamID Lowerbound Mean Upperbound Significance
CSJ1 CSJ2 0.2009 0.6667 1.1324 TRUE
CSJ1 HAFR 1.1801 1.6458 2.1116 TRUE
CSJ2 HAFR 0.5134 0.9792 1.4449 TRUE

download these results as csv

File:2009 sqbsh1atask2mrr friedman mean ranks.pngγçä

Task 1a Summary Results by Query Group

Simple Hit/Miss Count

CSJ1 CSJ2 HAFR
1 0.89 0.84 0.89
2 0.71 0.5 0.36
3 0.64 0.64 0.64
4 0.8 0.8 0.67
5 0.93 0.73 0.87
6 0.92 0.75 0.67
7 0.93 0.87 0.47
8 0.93 0.93 0.6
9 0.92 0.92 0.73
10 0.74 0.84 0.84
11 0.94 0.89 0.8
12 0.96 0.95 0.92
13 0.98 0.97 0.88
14 0.98 0.97 0.92
15 0.96 0.96 0.78
16 0.93 0.9 0.93
17 0.97 0.85 0.73
18 0.99 0.96 0.75
19 0.97 0.94 0.89
20 0.99 0.96 0.46
21 0.88 0.79 0.32
22 0.97 0.97 0.95
23 0.98 0.98 0.85
24 0.83 0.82 0.58
25 0.72 0.45 0.57
26 0.98 0.92 0.65
27 0.95 0.98 0.76
28 0.87 0.69 0.49
29 0.98 0.97 0.57
30 0.97 0.94 0.94
31 0.97 0.95 0.94
32 0.96 0.93 0.8
33 0.92 0.86 0.5
34 0.93 0.91 0.64
35 0.95 0.87 0.76
36 0.93 0.91 0.91
37 0.92 0.91 0.88
38 0.95 0.95 0.78
39 0.89 0.83 0.82
40 0.94 0.9 0.44
41 0.94 0.93 0.9
42 0.96 0.93 0.86
43 0.95 0.94 0.9
44 0.93 0.94 0.88
45 0.95 0.91 0.83
46 0.95 0.92 0.8
47 1 0.97 0.93
48 1 0.96 0.81

download these results as csv

MRR Method

CSJ1 CSJ2 HAFR
1 0.82 0.77 0.82
2 0.61 0.5 0.36
3 0.64 0.64 0.58
4 0.8 0.8 0.62
5 0.82 0.7 0.72
6 0.79 0.62 0.59
7 0.89 0.87 0.47
8 0.88 0.93 0.52
9 0.88 0.83 0.64
10 0.7 0.84 0.72
11 0.92 0.85 0.72
12 0.94 0.94 0.79
13 0.96 0.94 0.74
14 0.95 0.9 0.81
15 0.94 0.96 0.7
16 0.9 0.89 0.9
17 0.91 0.7 0.56
18 0.97 0.93 0.64
19 0.96 0.91 0.8
20 0.98 0.95 0.3
21 0.75 0.69 0.24
22 0.95 0.95 0.91
23 0.96 0.96 0.71
24 0.78 0.75 0.49
25 0.58 0.35 0.4
26 0.97 0.9 0.57
27 0.95 0.95 0.58
28 0.74 0.58 0.38
29 0.96 0.95 0.43
30 0.96 0.9 0.88
31 0.97 0.94 0.84
32 0.93 0.89 0.64
33 0.88 0.81 0.36
34 0.91 0.87 0.53
35 0.93 0.86 0.67
36 0.93 0.91 0.84
37 0.87 0.86 0.8
38 0.9 0.91 0.63
39 0.86 0.79 0.69
40 0.89 0.85 0.31
41 0.91 0.91 0.82
42 0.94 0.91 0.76
43 0.91 0.85 0.8
44 0.92 0.93 0.84
45 0.93 0.9 0.77
46 0.92 0.9 0.74
47 0.98 0.94 0.83
48 0.95 0.95 0.7

download these results as csv

Task 1a Summary Results by Query

Simple Hit/Miss Counting [1]

MRR Method [2]

Task 1b, ThinkIT's dataset Results

Task 1b Overall Results
CSJ1 CSJ2 HAFR
Simple Count 0.43 0.86 0.78
MRR 0.41 0.8 0.68
Total count 355 355 355

download these results as csv

Task 1b Friedman's Test for Significant Differences

The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups. Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);

Simple Hit/Miss Count:

TeamID TeamID Lowerbound Mean Upperbound Significance
CSJ2 HAFR -0.0394 0.2217 0.4828 FALSE
CSJ2 CSJ1 0.7058 0.9670 1.2281 TRUE
HAFR CSJ1 0.4841 0.7453 1.0064 TRUE

download these results as csv

File:2009 sqbsh1btask2simple friedman mean ranks.pngγçä

MRR Method:

TeamID TeamID Lowerbound Mean Upperbound Significance
CSJ2 HAFR 0.0442 0.3208 0.5974 TRUE
CSJ2 CSJ1 0.6904 0.9670 1.2436 TRUE
HAFR CSJ1 0.3696 0.6462 0.9228 TRUE

download these results as csv

File:2009 sqbsh1btask2mrr friedman mean ranks.pngγçä

Task 1b Summary Results by Query Group

Simple Hit/Miss Count

CSJ1 CSJ2 HAFR
1 0.55 1 0.82
2 0.6 0.8 0.8
3 0 0 1
4 0 1 1
5 0 0 0
6 0 0.83 0.83
7 1 1 0.83
8 0.33 0.33 0
9 0 0.8 1
10 0 1 1
11 0 1 0.5
12 1 1 1
13 0 0.6 0.6
14 0 0.5 0.5
15 0 0.5 0
16 1 0.5 1
17 0 0 0
18 0.5 1 0.5
19 0 1 1
20 0 0 1
21 0 0 0.5
22 0.44 0.89 0.89
23 1 1 1
24 0 1 1
25 0.5 1 1
26 0.64 0.91 0.91
27 0.5 1 0
28 0.5 1 0.5
29 1 1 1
30 0.75 1 0.75
31 1 1 1
32 0 1 0.86
33 0 1 1
34 0.5 0.5 0
35 0 1 1
36 0 0.8 0.6
37 0 0 0
38 0.5 1 1
39 0.43 1 1
40 1 1 0.5
41 0.13 0.88 0.63
42 0.67 0.92 0.5
43 0.67 0.67 0.56
44 0.56 1 1
45 1 1 0.67
46 0.5 0.75 0.75
47 0.75 1 1
48 1 1 1
49 1 1 1
50 1 1 1
51 0.5 0.75 1
52 0.25 1 0.75
53 0.33 1 0.83
54 0.67 1 1
55 0 1 0.75
56 0.5 0.5 0.5
57 1 1 0.4
58 0 1 1
59 0 1 1
60 1 1 0.5
61 0 1 1
62 0.33 1 1
63 1 1 1
64 0 1 0
65 0 0.67 1
66 0.5 0.75 1
67 0 1 0.67
68 1 1 1
69 0 1 1
70 1 1 1
71 0 0.67 1
72 1 1 1
73 1 1 0.5
74 1 1 1
75 0.33 0.67 0.67
76 0 1 0.5
77 1 1 1
78 1 1 1
79 0.5 1 1
80 0.33 1 1
81 0.33 0.5 1
82 0 1 1
83 0 1 1
84 0 0.25 0.5
85 0.5 1 0.75
86 1 1 1
87 0 1 0.33
88 0 1 1
89 0.43 0.86 1
90 0 1 1
91 0 1 0.5
92 0.5 1 1
93 0.5 1 1
94 0 1 1
95 0 1 0
96 1 0 1
97 0 1 1
98 0 1 1
99 0.75 1 1
100 0.33 1 1
101 0 0 0
102 0 1 1
103 0.38 0.75 0.63
104 0 1 1
105 1 0 1
106 0 0 0

download these results as csv

MRR Method

CSJ1 CSJ2 HAFR
1 0.42 0.95 0.82
2 0.6 0.8 0.55
3 0 0 1
4 0 0.67 0.5
5 0 0 0
6 0 0.83 0.83
7 1 0.92 0.83
8 0.04 0.33 0
9 0 0.8 0.85
10 0 1 1
11 0 0.75 0.17
12 1 1 1
13 0 0.6 0.3
14 0 0.5 0.5
15 0 0.5 0
16 1 0.5 1
17 0 0 0
18 0.5 1 0.5
19 0 1 1
20 0 0 0.57
21 0 0 0.5
22 0.44 0.81 0.65
23 1 1 1
24 0 1 1
25 0.5 1 0.58
26 0.64 0.66 0.82
27 0.38 0.88 0
28 0.5 0.78 0.5
29 1 1 1
30 0.75 1 0.75
31 1 1 1
32 0 1 0.79
33 0 1 1
34 0.17 0.25 0
35 0 1 1
36 0 0.8 0.6
37 0 0 0
38 0.5 1 1
39 0.43 0.93 0.7
40 1 1 0.5
41 0.13 0.76 0.56
42 0.67 0.92 0.42
43 0.57 0.67 0.46
44 0.56 1 0.85
45 1 1 0.67
46 0.5 0.5 0.5
47 0.75 1 1
48 1 1 1
49 1 1 0.5
50 1 1 1
51 0.5 0.54 1
52 0.25 1 0.75
53 0.33 0.85 0.83
54 0.58 1 1
55 0 1 0.36
56 0.5 0.5 0.5
57 1 1 0.23
58 0 1 1
59 0 1 0.46
60 1 1 0.5
61 0 1 1
62 0.33 1 0.83
63 1 0.75 1
64 0 1 0
65 0 0.67 1
66 0.5 0.75 1
67 0 1 0.37
68 1 1 0.63
69 0 1 1
70 1 1 1
71 0 0.42 1
72 1 1 1
73 1 1 0.5
74 1 1 1
75 0.33 0.4 0.67
76 0 1 0.5
77 1 1 0.78
78 1 1 0.2
79 0.5 1 1
80 0.33 1 1
81 0.33 0.35 1
82 0 1 1
83 0 0.71 1
84 0 0.08 0.09
85 0.5 1 0.63
86 1 1 1
87 0 1 0.33
88 0 1 1
89 0.43 0.59 0.87
90 0 1 0.29
91 0 0.58 0.5
92 0.5 1 1
93 0.5 1 1
94 0 1 1
95 0 0.25 0
96 0.5 0 1
97 0 1 1
98 0 1 0.33
99 0.75 0.68 0.78
100 0.33 1 1
101 0 0 0
102 0 0.33 1
103 0.38 0.75 0.46
104 0 1 1
105 0.1 0 0.14
106 0 0 0

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Task 1b Summary Results by Query

Simple Hit/Miss Counting [3]

MRR Method [4]

Task 1c, IOACAS2's dataset Results

As argued by Jang, there are some abnormal in the IOACAS2 data set. Therefore, we just provide the raw result without any further analysis.


Task 1c Summary Results by Query Group

Simple Hit/Miss Count

CSJ1 CSJ2 HAFR
1 0 1 0
2 0 0 1
3 0 1 1
4 0 0 0.33
5 0.27 0.27 0.36
6 0 0 0
7 0.5 0 0.5
8 0 0 0
9 0 0 1
10 1 1 1
11 0 0.5 0.75
12 0 1 1
13 0.14 0.14 0.43
14 0 0 0.4
15 0 0 0
16 1 1 1
17 0 0 1
18 0 1 0
19 0.5 0 0
20 0.25 0 0.5
21 0 0 1
22 0 0 0
23 0 0 0
24 0 0.14 0.14
25 0 0 0
26 0 0.25 1
27 0 1 0
28 0 0 0
29 0.5 0.38 0.63
30 0 0 1
31 0 0 0
32 0 0 1
33 0.33 0 0.33
34 0 1 1
35 0 1 0
36 0 0 1
37 0.11 0.33 0.89
38 0.5 0.5 0.5
39 0 1 1
40 0.5 0.5 1
41 0 1 1
42 0 0 1
43 0 0 1
44 0.5 1 1
45 0 0 0
46 0 0 1
47 0 1 1
48 0 1 0
49 0 0 0
50 0.13 0 0.88
51 0.25 0.5 0.75
52 0.33 0.33 0.67
53 0 0 0.5
54 0 0 0.5
55 0 0 1
56 0.33 0.33 0
57 0 0.33 1
58 0 0 0.29
59 1 1 0
60 0 1 1
61 0 0.5 0.5
62 0 0 0
63 0 0 0
64 0.44 0.33 0.33
65 0 0 0
66 0 0 0
67 0 0 0.5
68 0.6 0.7 0.5
69 0 0 0.33
70 0 0.6 0.3
71 0 0 1
72 0.5 0.33 0.33
73 0 0 0
74 0 0 0
75 0 0 1
76 0 0 0
77 1 1 1
78 0.83 0 0.17
79 0 0 1
80 0.54 0.62 0.85
81 0 0 1
82 0 0 0
83 0.43 0.14 1
84 0 0 0
85 0 0 1
86 0 1 0.25
87 1 1 1
88 0.25 0.75 0.75
89 0 1 1
90 0 0 0
91 0.5 0.5 1
92 0.5 1 1
93 0.4 0.6 0.8
94 0 0 0
95 0 0 1
96 0.33 0.33 1
97 0 0.5 1
98 1 0.5 0.5
99 0 0 0
100 0 0 1
101 0 1 1
102 0.5 0.5 0.5
103 0 1 0
104 1 0 1
105 0 1 0
106 0 0 0
107 0 1 1
108 0 1 1
109 1 0.5 1
110 0 0 1
111 0 0 0
112 0 0 0.5
113 0 0 0
114 1 1 1
115 0 0 0.5
116 0 0 0.5
117 0 0 0
118 1 0 1
119 0 0.2 0.4
120 0 0 1
121 0 0 0
122 1 0 1
123 0 1 1
124 0 0 0
125 0 1 1
126 0 0 0
127 0 0 0
128 0 0 0
129 0 1 1
130 0 0 0
131 1 1 1
132 0 0 0.5
133 1 1 0
134 1 1 1
135 0 0 0
136 0 1 1
137 0 1 1
138 0 1 1
139 1 1 1
140 1 0 1
141 1 1 1
142 0 0.5 1
143 0 0 1
144 0 0 1
145 0.33 0.33 1
146 0 0 0
147 0 1 0
148 0.5 1 0.5
149 1 1 1
150 0.5 0.5 1
151 0 1 1
152 0.33 0.33 1
153 0 0 1
154 0 1 0
155 0 0 1
156 1 0 1
157 1 1 1
158 0 0 0
159 0 0 0
160 0 0 0
161 0 0 0
162 1 1 0
163 0 0 1
164 0 1 0
165 0 1 1
166 0 0 0
167 1 1 1
168 1 1 0
169 1 1 1
170 1 1 1
171 0 0 1
172 1 1 1
173 1 0 1
174 1 0 1
175 0 1 1
176 0 1 1
177 0 0 1
178 1 0.5 1
179 1 0 1
180 0 0 1
181 1 1 1
182 0 0 1
183 0 1 1
184 0 1 0
185 0 1 1
186 0 0.33 0.33
187 0 1 1
188 0 0 1
189 0 1 1
190 0 0.5 1
191 1 1 1
192 0 1 0

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MRR Method

CSJ1 CSJ2 HAFR
1 0 0.17 0
2 0 0 0.58
3 0 1 1
4 0 0 0.33
5 0.15 0.2 0.19
6 0 0 0
7 0.17 0 0.5
8 0 0 0
9 0 0 1
10 1 1 1
11 0 0.28 0.63
12 0 1 1
13 0.02 0.04 0.43
14 0 0 0.4
15 0 0 0
16 1 0.25 1
17 0 0 0.63
18 0 0.17 0
19 0.06 0 0
20 0.03 0 0.5
21 0 0 1
22 0 0 0
23 0 0 0
24 0 0.02 0.01
25 0 0 0
26 0 0.13 0.57
27 0 0.25 0
28 0 0 0
29 0.39 0.26 0.41
30 0 0 1
31 0 0 0
32 0 0 0.75
33 0.33 0 0.17
34 0 0.2 1
35 0 0.14 0
36 0 0 0.75
37 0.01 0.08 0.75
38 0.25 0.25 0.5
39 0 0.13 1
40 0.5 0.5 1
41 0 1 1
42 0 0 1
43 0 0 0.78
44 0.25 0.6 1
45 0 0 0
46 0 0 1
47 0 0.25 0.33
48 0 1 0
49 0 0 0
50 0.03 0 0.66
51 0.03 0.29 0.53
52 0.33 0.17 0.67
53 0 0 0.13
54 0 0 0.5
55 0 0 0.14
56 0.06 0.08 0
57 0 0.08 0.16
58 0 0 0.29
59 1 0.14 0
60 0 1 0.33
61 0 0.25 0.25
62 0 0 0
63 0 0 0
64 0.36 0.22 0.2
65 0 0 0
66 0 0 0
67 0 0 0.07
68 0.55 0.36 0.43
69 0 0 0.33
70 0 0.6 0.3
71 0 0 1
72 0.5 0.13 0.13
73 0 0 0
74 0 0 0
75 0 0 0.6
76 0 0 0
77 1 0.5 1
78 0.42 0 0.02
79 0 0 1
80 0.4 0.43 0.55
81 0 0 0.8
82 0 0 0
83 0.17 0.02 0.81
84 0 0 0
85 0 0 0.5
86 0 0.79 0.13
87 0.5 1 1
88 0.06 0.63 0.75
89 0 1 1
90 0 0 0
91 0.07 0.5 1
92 0.5 1 1
93 0.25 0.43 0.7
94 0 0 0
95 0 0 1
96 0.33 0.17 0.78
97 0 0.5 1
98 0.57 0.5 0.5
99 0 0 0
100 0 0 0.67
101 0 1 0.25
102 0.5 0.5 0.44
103 0 1 0
104 0.2 0 0.14
105 0 1 0
106 0 0 0
107 0 0.67 0.67
108 0 1 1
109 0.63 0.5 0.56
110 0 0 1
111 0 0 0
112 0 0 0.5
113 0 0 0
114 1 1 1
115 0 0 0.5
116 0 0 0.05
117 0 0 0
118 0.5 0 1
119 0 0.2 0.13
120 0 0 1
121 0 0 0
122 0.5 0 0.14
123 0 0.2 1
124 0 0 0
125 0 1 1
126 0 0 0
127 0 0 0
128 0 0 0
129 0 0.1 1
130 0 0 0
131 1 1 1
132 0 0 0.5
133 1 1 0
134 1 1 0.33
135 0 0 0
136 0 1 1
137 0 0.14 0.33
138 0 0.61 1
139 1 1 1
140 0.14 0 1
141 1 0.33 1
142 0 0.12 0.58
143 0 0 0.11
144 0 0 0.5
145 0.04 0.17 1
146 0 0 0
147 0 0.11 0
148 0.5 1 0.5
149 1 1 1
150 0.5 0.25 0.63
151 0 0.33 1
152 0.03 0.33 0.83
153 0 0 1
154 0 0.5 0
155 0 0 1
156 0.2 0 0.33
157 1 1 1
158 0 0 0
159 0 0 0
160 0 0 0
161 0 0 0
162 1 0.5 0
163 0 0 0.25
164 0 0.25 0
165 0 0.2 1
166 0 0 0
167 1 1 1
168 1 0.17 0
169 1 1 1
170 0.2 0.5 1
171 0 0 0.11
172 1 1 0.5
173 0.17 0 1
174 0.2 0 0.25
175 0 0.5 1
176 0 0.13 1
177 0 0 1
178 1 0.1 0.63
179 1 0 0.33
180 0 0 1
181 1 0.5 1
182 0 0 1
183 0 1 0.25
184 0 1 0
185 0 1 1
186 0 0.33 0.03
187 0 1 0.25
188 0 0 1
189 0 1 1
190 0 0.06 1
191 1 0.17 1
192 0 1 0

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Task 1c Summary Results by Query

Simple Hit/Miss Counting [5]

MRR Method [6]

Task 2 Results

Task 2 Overall Results
CSJ1 CSJ2 HAFR
Simple Count 8.48 6.97 6.98
Total count 2040 2040 2020

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Task 2 Friedman's Test for Significant Differences

The Friedman test was run in MATLAB against the QBSH Task 1 MRR data over the 48 ground truth song groups. Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);

TeamID TeamID Lowerbound Mean Upperbound Significance
CSJ1 CSJ2 0.8116 1.2812 1.7509 TRUE
CSJ1 HAFR 0.8741 1.3438 1.8134 TRUE
CSJ2 HAFR -0.4071 0.0625 0.5321 FALSE

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File:2009 sqbshtask2 friedman mean ranks.png γçä


Task 2 Summary Results by Query Group
CSJ1 CSJ2 HAFR
1 4.56 3.67 4.11
2 2 1.75 0.25
3 0 0 0
4 3.4 3.4 2.6
5 6.6 6.6 4.8
6 4 2.5 1.5
7 5.8 4.4 2.2
8 5.4 6.2 4
9 2 1.5 2.5
10 4.33 2.56 3
11 8.3 7.56 6.33
12 9.25 7.54 6.93
13 9.22 7.57 7.35
14 8.63 7.79 8.69
15 4.36 3.93 3.93
16 8.43 6.35 7.38
17 8.67 7.15 5.41
18 8.39 6.03 8.14
19 9.22 7.94 7.69
20 9.1 8.12 8.27
21 8.91 6 3.26
22 9.17 7.82 8.1
23 8.29 6.87 4.93
24 8.9 7.61 6.86
25 8.1 7.39 7.45
26 7.52 4.86 6
27 7.93 6.42 4.76
28 8.98 7.93 7.07
29 9.04 8.03 7.31
30 9.42 8.03 9.05
31 9.57 8.7 7.83
32 9.59 8.08 7.84
33 7.59 6.1 6.52
34 9.29 7.51 8.38
35 8.74 6.43 7.5
36 7.71 5.79 4.33
37 7.43 5.21 6.25
38 8.28 7.11 7.7
39 6.97 5.91 7.19
40 8.81 6.96 6.44
41 9.15 7.14 7.59
42 6.86 6.12 5.71
43 8.2 5.88 6.68
44 9.19 8.11 6.56
45 7.11 5.13 5.09
46 7.66 5.42 6.7
47 7.92 5.69 3.38
48 8.18 6 7.59

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Task 2 Summary Results by Query

[7]

Runtime Results

Participant Task Runtime (min) Machine
CSJ1 1a ~55 BIGWIN
CSJ1 1b 7 BIGWIN
CSJ1 2 ~740 BIGWIN
CSJ2 1a ~1800 BIGWIN
CSJ2 1b ~210 BIGWIN
CSJ2 2 ~730 BIGWIN
HAFR 1a 1689 BEER 2
HAFR 1b 247 BEER 2
HAFR 2 242 BEER 2

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