2011:Query-by-Singing/Humming Results
Contents
- 1 Introduction
- 2 General Legend
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 2011: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:
- Jang's Dataset Results Roger Jang's MIR-QBSH corpus with 48 songs as ground truth + 2000 Essen Collection MIDI noise files. See ESAC Data Homepage for more information about the Essen Collection. The queries consists of 4431 humming. All queries are from the beginning of references
- ThinkIT's Dataset Results IOACAS corpus 1 data set with 106 songs as ground truth + 2000 Essen Collection MIDI noise files. See ESAC Data Homepage for more information about the Essen Collection. The queries consists of 355 humming. There are no "singing from beginning" gurantee.
- IOACAS's 2nd Dataset Results IOACAS corpus 2 data set with 192 songs as ground truth + 2000 Essen Collection MIDI noise files. See 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 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
Sub code | Submission name | Abstract | Contributors |
---|---|---|---|
TY1 | QBSH algorithm 1 | Tzu-Chun Yeh | |
TY2 | QBSH algorithm 2 | Tzu-Chun Yeh | |
JSSLP1 | KETI | Dalwon Jang, SUNG JOO PARK,Chai-Jong Song,Saim Shin,SEI-JIN JANG,Jong-Seol Lee,Seok-Pil Lee,Kyeung Hak Seo |
Task 1 Results
Task 1a, Jang's dataset Results
Task 1a Overall Results
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
Simple Count | 0.939 | 0.956 | 0.919 |
MRR | 0.897 | 0.93 | 0.881 |
Total Count | 4431 | 4431 | 4431 |
Task 1a Friedman's Test for Significant Differences
The Friedman test was run in MATLAB against the QBSH Task 1a Simple/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 |
---|---|---|---|---|---|
TY1 | JSSLP1 | -0.0749 | 0.3438 | 0.7624 | FALSE |
TY1 | TY2 | 0.6439 | 1.0625 | 1.4811 | TRUE |
JSSLP1 | TY2 | 0.3001 | 0.7188 | 1.1374 | TRUE |
MRR Method:
TeamID | TeamID | Lowerbound | Mean | Upperbound | Significance |
---|---|---|---|---|---|
TY1 | JSSLP1 | 0.2321 | 0.6979 | 1.1637 | TRUE |
TY1 | TY2 | 0.7426 | 1.2083 | 1.6741 | TRUE |
JSSLP1 | TY2 | 0.0446 | 0.5104 | 0.9762 | TRUE |
Task 1a Summary Results by Query Group
Simple Hit/Miss Count
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 0.947 | 0.947 | 0.895 |
2 | 0.714 | 0.786 | 0.643 |
3 | 0.727 | 0.727 | 0.727 |
4 | 0.8 | 0.8 | 0.8 |
5 | 0.933 | 0.933 | 0.8 |
6 | 0.833 | 0.833 | 0.917 |
7 | 0.867 | 0.933 | 0.867 |
8 | 0.933 | 0.933 | 0.933 |
9 | 0.917 | 0.917 | 0.917 |
10 | 0.947 | 0.684 | 0.842 |
11 | 0.927 | 0.948 | 0.896 |
12 | 0.968 | 0.968 | 0.961 |
13 | 0.95 | 0.981 | 0.963 |
14 | 0.975 | 0.994 | 0.956 |
15 | 0.963 | 0.963 | 0.963 |
16 | 0.931 | 0.969 | 0.925 |
17 | 0.941 | 0.98 | 0.928 |
18 | 0.97 | 0.976 | 0.957 |
19 | 0.949 | 0.968 | 0.943 |
20 | 0.971 | 1 | 0.982 |
21 | 0.742 | 0.818 | 0.758 |
22 | 0.977 | 0.988 | 0.977 |
23 | 1 | 0.984 | 0.952 |
24 | 0.752 | 0.843 | 0.785 |
25 | 0.756 | 0.862 | 0.715 |
26 | 0.962 | 0.962 | 0.962 |
27 | 0.965 | 0.965 | 0.965 |
28 | 0.879 | 0.879 | 0.725 |
29 | 0.993 | 0.987 | 0.973 |
30 | 0.959 | 0.986 | 0.938 |
31 | 0.979 | 0.993 | 0.972 |
32 | 0.964 | 0.956 | 0.934 |
33 | 0.94 | 0.94 | 0.862 |
34 | 0.949 | 0.927 | 0.891 |
35 | 0.926 | 0.966 | 0.886 |
36 | 0.953 | 0.93 | 0.953 |
37 | 0.939 | 0.939 | 0.848 |
38 | 0.965 | 0.93 | 0.956 |
39 | 0.952 | 0.98 | 0.939 |
40 | 0.93 | 0.972 | 0.915 |
41 | 0.944 | 0.984 | 0.913 |
42 | 0.957 | 0.946 | 0.946 |
43 | 0.951 | 0.976 | 0.943 |
44 | 0.95 | 0.95 | 0.95 |
45 | 0.97 | 0.97 | 0.939 |
46 | 0.989 | 0.978 | 0.935 |
47 | 0.931 | 1 | 0.931 |
48 | 0.962 | 1 | 0.925 |
MRR Method
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 0.947 | 0.901 | 0.847 |
2 | 0.714 | 0.732 | 0.595 |
3 | 0.682 | 0.682 | 0.645 |
4 | 0.8 | 0.8 | 0.8 |
5 | 0.933 | 0.85 | 0.767 |
6 | 0.833 | 0.833 | 0.808 |
7 | 0.867 | 0.889 | 0.867 |
8 | 0.933 | 0.88 | 0.933 |
9 | 0.917 | 0.917 | 0.844 |
10 | 0.947 | 0.61 | 0.842 |
11 | 0.876 | 0.932 | 0.868 |
12 | 0.945 | 0.958 | 0.937 |
13 | 0.917 | 0.94 | 0.938 |
14 | 0.865 | 0.969 | 0.879 |
15 | 0.944 | 0.907 | 0.93 |
16 | 0.889 | 0.922 | 0.891 |
17 | 0.905 | 0.95 | 0.857 |
18 | 0.959 | 0.965 | 0.947 |
19 | 0.931 | 0.968 | 0.925 |
20 | 0.929 | 0.977 | 0.944 |
21 | 0.632 | 0.722 | 0.678 |
22 | 0.973 | 0.984 | 0.966 |
23 | 0.965 | 0.962 | 0.939 |
24 | 0.688 | 0.78 | 0.694 |
25 | 0.541 | 0.79 | 0.595 |
26 | 0.942 | 0.946 | 0.939 |
27 | 0.928 | 0.965 | 0.951 |
28 | 0.776 | 0.798 | 0.597 |
29 | 0.953 | 0.968 | 0.959 |
30 | 0.934 | 0.969 | 0.91 |
31 | 0.965 | 0.988 | 0.944 |
32 | 0.93 | 0.956 | 0.891 |
33 | 0.886 | 0.92 | 0.802 |
34 | 0.882 | 0.908 | 0.85 |
35 | 0.887 | 0.948 | 0.876 |
36 | 0.935 | 0.93 | 0.953 |
37 | 0.908 | 0.903 | 0.833 |
38 | 0.949 | 0.906 | 0.923 |
39 | 0.921 | 0.946 | 0.882 |
40 | 0.878 | 0.914 | 0.866 |
41 | 0.922 | 0.948 | 0.898 |
42 | 0.895 | 0.935 | 0.91 |
43 | 0.925 | 0.943 | 0.904 |
44 | 0.923 | 0.944 | 0.938 |
45 | 0.962 | 0.954 | 0.916 |
46 | 0.913 | 0.948 | 0.92 |
47 | 0.931 | 0.955 | 0.931 |
48 | 0.945 | 0.95 | 0.915 |
Task 1a Summary Results by Query
Simple Hit/Miss Counting [1]
MRR Method [2]
Task 1b, ThinkIT's dataset Results
Task 1b Overall Results
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
Simple Count | 0.941 | 0.456 | 0.899 |
MRR | 0.912 | 0.437 | 0.853 |
Total Count | 355 | 355 | 355 |
Task 1b Friedman's Test for Significant Differences
The Friedman test was run in MATLAB against the QBSH Task 1b Simple/MRR data over the 106 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 |
---|---|---|---|---|---|
JSSLP1 | TY2 | -0.0922 | 0.1604 | 0.4130 | FALSE |
JSSLP1 | TY1 | 0.8465 | 1.0991 | 1.3516 | TRUE |
TY2 | TY1 | 0.6861 | 0.9387 | 1.1913 | TRUE |
MRR Method:
TeamID | TeamID | Lowerbound | Mean | Upperbound | Significance |
---|---|---|---|---|---|
JSSLP1 | TY2 | 0.0002 | 0.2642 | 0.5281 | TRUE |
JSSLP1 | TY1 | 0.8729 | 1.1368 | 1.4007 | TRUE |
TY2 | TY1 | 0.6087 | 0.8726 | 1.1366 | TRUE |
Task 1b Summary Results by Query Group
Simple Hit/Miss Count
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 1 | 0.636 | 1 |
2 | 1 | 0.6 | 0.8 |
3 | 0 | 0 | 1 |
4 | 1 | 1 | 1 |
5 | 0 | 0 | 1 |
6 | 0.833 | 0 | 0.667 |
7 | 1 | 1 | 1 |
8 | 1 | 0.333 | 0.667 |
9 | 1 | 0 | 1 |
10 | 1 | 0 | 1 |
11 | 1 | 0 | 0.5 |
12 | 1 | 1 | 1 |
13 | 0.8 | 0.2 | 0.6 |
14 | 1 | 0 | 1 |
15 | 1 | 0 | 0.5 |
16 | 1 | 1 | 0.75 |
17 | 0 | 0 | 0 |
18 | 1 | 0.5 | 1 |
19 | 1 | 0 | 1 |
20 | 1 | 0 | 0.5 |
21 | 1 | 0 | 0.5 |
22 | 1 | 0.444 | 0.889 |
23 | 1 | 1 | 1 |
24 | 1 | 0 | 1 |
25 | 1 | 0.5 | 1 |
26 | 1 | 0.636 | 0.909 |
27 | 1 | 0.5 | 1 |
28 | 1 | 0.5 | 0.75 |
29 | 1 | 1 | 1 |
30 | 1 | 1 | 1 |
31 | 1 | 1 | 1 |
32 | 1 | 0 | 1 |
33 | 1 | 0 | 1 |
34 | 0.5 | 0.5 | 0.5 |
35 | 1 | 0 | 1 |
36 | 0.8 | 0 | 0.8 |
37 | 1 | 0 | 0 |
38 | 1 | 0.5 | 1 |
39 | 1 | 0.429 | 1 |
40 | 1 | 1 | 1 |
41 | 0.875 | 0.25 | 0.875 |
42 | 1 | 0.667 | 0.917 |
43 | 0.889 | 0.667 | 0.778 |
44 | 1 | 0.556 | 1 |
45 | 1 | 1 | 1 |
46 | 0.75 | 0.5 | 0.75 |
47 | 1 | 0.75 | 1 |
48 | 1 | 1 | 1 |
49 | 1 | 1 | 1 |
50 | 1 | 1 | 1 |
51 | 1 | 0.5 | 1 |
52 | 1 | 0.5 | 0.75 |
53 | 1 | 0.333 | 1 |
54 | 1 | 0.833 | 1 |
55 | 1 | 0 | 1 |
56 | 0.5 | 0.5 | 0.5 |
57 | 1 | 1 | 1 |
58 | 1 | 0 | 1 |
59 | 1 | 0 | 1 |
60 | 0 | 1 | 1 |
61 | 1 | 0 | 1 |
62 | 1 | 0.333 | 1 |
63 | 1 | 1 | 1 |
64 | 1 | 0 | 1 |
65 | 1 | 0 | 0.667 |
66 | 1 | 0.5 | 0.75 |
67 | 0.333 | 0 | 1 |
68 | 1 | 1 | 1 |
69 | 1 | 0 | 1 |
70 | 1 | 1 | 1 |
71 | 1 | 0 | 1 |
72 | 1 | 1 | 1 |
73 | 1 | 1 | 1 |
74 | 1 | 1 | 1 |
75 | 1 | 0.333 | 0.667 |
76 | 0.5 | 0 | 1 |
77 | 1 | 1 | 1 |
78 | 1 | 1 | 1 |
79 | 1 | 0.5 | 1 |
80 | 0.333 | 0.333 | 1 |
81 | 1 | 0.333 | 1 |
82 | 1 | 0 | 1 |
83 | 1 | 0 | 1 |
84 | 0.75 | 0.25 | 0.5 |
85 | 1 | 0.5 | 1 |
86 | 1 | 1 | 1 |
87 | 1 | 0 | 1 |
88 | 1 | 0 | 1 |
89 | 1 | 0.571 | 0.714 |
90 | 1 | 0 | 1 |
91 | 1 | 0 | 1 |
92 | 1 | 0.5 | 1 |
93 | 1 | 0.5 | 1 |
94 | 1 | 0 | 1 |
95 | 1 | 0 | 1 |
96 | 1 | 1 | 0 |
97 | 1 | 0 | 1 |
98 | 1 | 0 | 1 |
99 | 1 | 0.75 | 1 |
100 | 1 | 0.333 | 1 |
101 | 0 | 0 | 0 |
102 | 1 | 0 | 1 |
103 | 1 | 0.375 | 1 |
104 | 1 | 0 | 1 |
105 | 1 | 1 | 0 |
106 | 0.667 | 0 | 0.667 |
MRR Method
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 1 | 0.5 | 1 |
2 | 1 | 0.6 | 0.8 |
3 | 0 | 0 | 1 |
4 | 0.667 | 0.163 | 0.563 |
5 | 0 | 0 | 0.25 |
6 | 0.75 | 0 | 0.667 |
7 | 1 | 1 | 0.917 |
8 | 1 | 0.333 | 0.667 |
9 | 1 | 0 | 0.9 |
10 | 1 | 0 | 1 |
11 | 1 | 0 | 0.5 |
12 | 1 | 1 | 1 |
13 | 0.8 | 0.2 | 0.6 |
14 | 1 | 0 | 0.625 |
15 | 0.667 | 0 | 0.5 |
16 | 1 | 1 | 0.542 |
17 | 0 | 0 | 0 |
18 | 1 | 0.5 | 1 |
19 | 1 | 0 | 1 |
20 | 0.375 | 0 | 0.1 |
21 | 1 | 0 | 0.083 |
22 | 0.926 | 0.389 | 0.806 |
23 | 1 | 1 | 1 |
24 | 1 | 0 | 1 |
25 | 1 | 0.5 | 1 |
26 | 1 | 0.636 | 0.909 |
27 | 1 | 0.5 | 1 |
28 | 1 | 0.375 | 0.75 |
29 | 1 | 1 | 1 |
30 | 1 | 1 | 1 |
31 | 1 | 1 | 1 |
32 | 1 | 0 | 1 |
33 | 0.9 | 0 | 1 |
34 | 0.5 | 0.5 | 0.5 |
35 | 1 | 0 | 1 |
36 | 0.8 | 0 | 0.8 |
37 | 0.333 | 0 | 0 |
38 | 1 | 0.5 | 1 |
39 | 1 | 0.429 | 1 |
40 | 1 | 1 | 1 |
41 | 0.875 | 0.167 | 0.875 |
42 | 1 | 0.667 | 0.917 |
43 | 0.833 | 0.667 | 0.722 |
44 | 1 | 0.556 | 1 |
45 | 1 | 1 | 1 |
46 | 0.625 | 0.5 | 0.75 |
47 | 1 | 0.75 | 1 |
48 | 1 | 1 | 1 |
49 | 1 | 1 | 1 |
50 | 1 | 1 | 1 |
51 | 1 | 0.5 | 0.75 |
52 | 1 | 0.281 | 0.75 |
53 | 1 | 0.333 | 0.861 |
54 | 1 | 0.75 | 1 |
55 | 1 | 0 | 1 |
56 | 0.5 | 0.5 | 0.5 |
57 | 1 | 1 | 1 |
58 | 1 | 0 | 1 |
59 | 1 | 0 | 1 |
60 | 0 | 1 | 1 |
61 | 1 | 0 | 0.25 |
62 | 1 | 0.333 | 1 |
63 | 1 | 1 | 1 |
64 | 1 | 0 | 1 |
65 | 1 | 0 | 0.667 |
66 | 1 | 0.5 | 0.75 |
67 | 0.333 | 0 | 1 |
68 | 0.625 | 1 | 0.556 |
69 | 1 | 0 | 1 |
70 | 1 | 1 | 1 |
71 | 1 | 0 | 0.722 |
72 | 1 | 1 | 1 |
73 | 1 | 1 | 1 |
74 | 1 | 1 | 1 |
75 | 1 | 0.333 | 0.444 |
76 | 0.5 | 0 | 1 |
77 | 1 | 1 | 1 |
78 | 1 | 1 | 1 |
79 | 1 | 0.5 | 1 |
80 | 0.333 | 0.333 | 1 |
81 | 1 | 0.333 | 0.857 |
82 | 1 | 0 | 1 |
83 | 0.833 | 0 | 0.611 |
84 | 0.55 | 0.125 | 0.5 |
85 | 1 | 0.5 | 1 |
86 | 1 | 1 | 1 |
87 | 1 | 0 | 1 |
88 | 1 | 0 | 1 |
89 | 0.881 | 0.571 | 0.643 |
90 | 1 | 0 | 1 |
91 | 1 | 0 | 1 |
92 | 1 | 0.5 | 1 |
93 | 1 | 0.5 | 1 |
94 | 1 | 0 | 1 |
95 | 1 | 0 | 1 |
96 | 1 | 1 | 0 |
97 | 1 | 0 | 1 |
98 | 1 | 0 | 1 |
99 | 1 | 0.75 | 1 |
100 | 1 | 0.333 | 1 |
101 | 0 | 0 | 0 |
102 | 1 | 0 | 1 |
103 | 1 | 0.375 | 0.938 |
104 | 1 | 0 | 1 |
105 | 0.25 | 1 | 0 |
106 | 0.444 | 0 | 0.333 |
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
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 0 | 0 | 1 |
2 | 0 | 0 | 0 |
3 | 1 | 0 | 1 |
4 | 0 | 0 | 0 |
5 | 0.182 | 0.273 | 0.273 |
6 | 0 | 0 | 0 |
7 | 0 | 0.5 | 0.5 |
8 | 0 | 0 | 0 |
9 | 0 | 1 | 0 |
10 | 1 | 1 | 1 |
11 | 0 | 0 | 0.5 |
12 | 1 | 0 | 1 |
13 | 0 | 0.143 | 0.286 |
14 | 0.2 | 0.2 | 0.4 |
15 | 0 | 0 | 0 |
16 | 0 | 1 | 1 |
17 | 0 | 0 | 0 |
18 | 0 | 0 | 1 |
19 | 0 | 0 | 0.5 |
20 | 0 | 0.5 | 0 |
21 | 0 | 0 | 0.5 |
22 | 0 | 0 | 0 |
23 | 0 | 0 | 0 |
24 | 0 | 0 | 0 |
25 | 0 | 0 | 0 |
26 | 0 | 0 | 0.25 |
27 | 0 | 0 | 0 |
28 | 0 | 0 | 0 |
29 | 0.125 | 0.375 | 0.375 |
30 | 1 | 0 | 0 |
31 | 0 | 0 | 0 |
32 | 0 | 0 | 0 |
33 | 0 | 0.333 | 0.333 |
34 | 0 | 0 | 0 |
35 | 0 | 1 | 1 |
36 | 0 | 0.5 | 0 |
37 | 0.111 | 0 | 0.556 |
38 | 0 | 0.5 | 0.5 |
39 | 0 | 0 | 1 |
40 | 0.5 | 0.5 | 0.5 |
41 | 1 | 1 | 1 |
42 | 0 | 0 | 0 |
43 | 0 | 0 | 0 |
44 | 0.5 | 0.5 | 1 |
45 | 0 | 0 | 0 |
46 | 0 | 0 | 0 |
47 | 0 | 0 | 1 |
48 | 0 | 0 | 1 |
49 | 0 | 0 | 0 |
50 | 0.375 | 0.375 | 0.25 |
51 | 0 | 0.25 | 0.25 |
52 | 0 | 0.333 | 0.333 |
53 | 0 | 0 | 0.5 |
54 | 0.5 | 0 | 0.25 |
55 | 1 | 0 | 0 |
56 | 1 | 0.667 | 0.333 |
57 | 0.333 | 0 | 0.333 |
58 | 0 | 0 | 0 |
59 | 0 | 1 | 1 |
60 | 1 | 0 | 1 |
61 | 0 | 0 | 0.5 |
62 | 0 | 0 | 0 |
63 | 1 | 0 | 0 |
64 | 0.333 | 0.333 | 0.333 |
65 | 1 | 1 | 0 |
66 | 1 | 0 | 0 |
67 | 0.5 | 0 | 0 |
68 | 1 | 0.6 | 0.6 |
69 | 1 | 0 | 0.333 |
70 | 0.6 | 0 | 0.7 |
71 | 1 | 0 | 0 |
72 | 0.833 | 0.667 | 0.5 |
73 | 0 | 0 | 0 |
74 | 0.333 | 0 | 0 |
75 | 0.5 | 0 | 0 |
76 | 0 | 0 | 0 |
77 | 1 | 1 | 0 |
78 | 0.667 | 0.833 | 0.5 |
79 | 0 | 0 | 0 |
80 | 0.615 | 0.615 | 0.692 |
81 | 0.2 | 0 | 0 |
82 | 0 | 0 | 0 |
83 | 0.571 | 0.857 | 0.143 |
84 | 0 | 0 | 0 |
85 | 1 | 0 | 0 |
86 | 1 | 0 | 1 |
87 | 1 | 1 | 1 |
88 | 0.25 | 0.25 | 0.75 |
89 | 1 | 0 | 1 |
90 | 0 | 0 | 0 |
91 | 0.5 | 0.5 | 0.5 |
92 | 1 | 0.5 | 1 |
93 | 1 | 0.4 | 0.8 |
94 | 0 | 1 | 0 |
95 | 0 | 0 | 1 |
96 | 0.667 | 0.667 | 0.333 |
97 | 1 | 0 | 1 |
98 | 1 | 1 | 1 |
99 | 0 | 1 | 0 |
100 | 0 | 0 | 0.5 |
101 | 0 | 0 | 1 |
102 | 0.875 | 0.5 | 0.375 |
103 | 1 | 0 | 1 |
104 | 1 | 1 | 0 |
105 | 1 | 0 | 1 |
106 | 0 | 0 | 0 |
107 | 0.5 | 0 | 1 |
108 | 1 | 0 | 1 |
109 | 1 | 1 | 0.5 |
110 | 0 | 0 | 1 |
111 | 0 | 0 | 0 |
112 | 0 | 0 | 0 |
113 | 0 | 0 | 0 |
114 | 1 | 1 | 1 |
115 | 1 | 1 | 0 |
116 | 0 | 0.5 | 0 |
117 | 0 | 0 | 0 |
118 | 1 | 1 | 0 |
119 | 0 | 0.2 | 0.2 |
120 | 0 | 0 | 0 |
121 | 0 | 0 | 0 |
122 | 1 | 1 | 0 |
123 | 1 | 0 | 1 |
124 | 0 | 0 | 0 |
125 | 1 | 0.5 | 1 |
126 | 0 | 0 | 0 |
127 | 0 | 0 | 0 |
128 | 0 | 0 | 0 |
129 | 1 | 0 | 1 |
130 | 0 | 0 | 0 |
131 | 0 | 1 | 1 |
132 | 0 | 0 | 0.5 |
133 | 1 | 1 | 1 |
134 | 1 | 1 | 1 |
135 | 1 | 0 | 0 |
136 | 1 | 0 | 1 |
137 | 1 | 0 | 1 |
138 | 1 | 0 | 0.667 |
139 | 1 | 1 | 1 |
140 | 1 | 1 | 0 |
141 | 1 | 1 | 1 |
142 | 1 | 0 | 0.75 |
143 | 1 | 0 | 0 |
144 | 1 | 0 | 0 |
145 | 0.333 | 0.333 | 0.333 |
146 | 0 | 0 | 0 |
147 | 1 | 0 | 1 |
148 | 1 | 0.5 | 1 |
149 | 1 | 1 | 1 |
150 | 1 | 0.5 | 1 |
151 | 1 | 0 | 1 |
152 | 0.667 | 0.333 | 0.333 |
153 | 1 | 0 | 0 |
154 | 1 | 0 | 1 |
155 | 0 | 0 | 1 |
156 | 1 | 1 | 0 |
157 | 1 | 1 | 1 |
158 | 0 | 0 | 0 |
159 | 0 | 0 | 0 |
160 | 0 | 0 | 0 |
161 | 0 | 1 | 0 |
162 | 0 | 1 | 0 |
163 | 0 | 0 | 0 |
164 | 1 | 0 | 1 |
165 | 1 | 0 | 0 |
166 | 0 | 0 | 0 |
167 | 1 | 1 | 1 |
168 | 1 | 1 | 1 |
169 | 1 | 1 | 1 |
170 | 1 | 1 | 1 |
171 | 0 | 0 | 0 |
172 | 1 | 1 | 1 |
173 | 1 | 1 | 1 |
174 | 1 | 1 | 0 |
175 | 1 | 0 | 1 |
176 | 1 | 0 | 1 |
177 | 0 | 0 | 0 |
178 | 1 | 1 | 0.5 |
179 | 1 | 1 | 1 |
180 | 1 | 0 | 0 |
181 | 1 | 1 | 1 |
182 | 1 | 0 | 0 |
183 | 1 | 0 | 1 |
184 | 0 | 0 | 1 |
185 | 0 | 1 | 1 |
186 | 1 | 0 | 0.667 |
187 | 1 | 0 | 1 |
188 | 0 | 1 | 0 |
189 | 0 | 0 | 1 |
190 | 1 | 0.5 | 1 |
191 | 1 | 1 | 0 |
192 | 0 | 0 | 1 |
MRR Method
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 0 | 0 | 1 |
2 | 0 | 0 | 0 |
3 | 1 | 0 | 1 |
4 | 0 | 0 | 0 |
5 | 0.025 | 0.132 | 0.133 |
6 | 0 | 0 | 0 |
7 | 0 | 0.5 | 0.083 |
8 | 0 | 0 | 0 |
9 | 0 | 1 | 0 |
10 | 1 | 1 | 1 |
11 | 0 | 0 | 0.3 |
12 | 1 | 0 | 1 |
13 | 0 | 0.143 | 0.163 |
14 | 0.04 | 0.022 | 0.073 |
15 | 0 | 0 | 0 |
16 | 0 | 1 | 0.5 |
17 | 0 | 0 | 0 |
18 | 0 | 0 | 0.2 |
19 | 0 | 0 | 0.071 |
20 | 0 | 0.092 | 0 |
21 | 0 | 0 | 0.063 |
22 | 0 | 0 | 0 |
23 | 0 | 0 | 0 |
24 | 0 | 0 | 0 |
25 | 0 | 0 | 0 |
26 | 0 | 0 | 0.042 |
27 | 0 | 0 | 0 |
28 | 0 | 0 | 0 |
29 | 0.042 | 0.375 | 0.292 |
30 | 0.333 | 0 | 0 |
31 | 0 | 0 | 0 |
32 | 0 | 0 | 0 |
33 | 0 | 0.167 | 0.083 |
34 | 0 | 0 | 0 |
35 | 0 | 0.1 | 1 |
36 | 0 | 0.5 | 0 |
37 | 0.037 | 0 | 0.347 |
38 | 0 | 0.5 | 0.167 |
39 | 0 | 0 | 0.5 |
40 | 0.5 | 0.5 | 0.25 |
41 | 1 | 0.1 | 0.5 |
42 | 0 | 0 | 0 |
43 | 0 | 0 | 0 |
44 | 0.5 | 0.5 | 0.75 |
45 | 0 | 0 | 0 |
46 | 0 | 0 | 0 |
47 | 0 | 0 | 0.25 |
48 | 0 | 0 | 0.5 |
49 | 0 | 0 | 0 |
50 | 0.156 | 0.153 | 0.104 |
51 | 0 | 0.025 | 0.25 |
52 | 0 | 0.333 | 0.111 |
53 | 0 | 0 | 0.5 |
54 | 0.5 | 0 | 0.031 |
55 | 1 | 0 | 0 |
56 | 1 | 0.25 | 0.167 |
57 | 0.333 | 0 | 0.042 |
58 | 0 | 0 | 0 |
59 | 0 | 1 | 1 |
60 | 1 | 0 | 1 |
61 | 0 | 0 | 0.25 |
62 | 0 | 0 | 0 |
63 | 1 | 0 | 0 |
64 | 0.333 | 0.333 | 0.235 |
65 | 0.25 | 0.5 | 0 |
66 | 1 | 0 | 0 |
67 | 0.167 | 0 | 0 |
68 | 0.808 | 0.511 | 0.448 |
69 | 1 | 0 | 0.167 |
70 | 0.513 | 0 | 0.7 |
71 | 1 | 0 | 0 |
72 | 0.833 | 0.542 | 0.357 |
73 | 0 | 0 | 0 |
74 | 0.063 | 0 | 0 |
75 | 0.125 | 0 | 0 |
76 | 0 | 0 | 0 |
77 | 1 | 0.333 | 0 |
78 | 0.431 | 0.597 | 0.274 |
79 | 0 | 0 | 0 |
80 | 0.492 | 0.547 | 0.564 |
81 | 0.1 | 0 | 0 |
82 | 0 | 0 | 0 |
83 | 0.214 | 0.619 | 0.048 |
84 | 0 | 0 | 0 |
85 | 1 | 0 | 0 |
86 | 1 | 0 | 0.813 |
87 | 0.143 | 1 | 0.333 |
88 | 0.25 | 0.25 | 0.625 |
89 | 1 | 0 | 1 |
90 | 0 | 0 | 0 |
91 | 0.5 | 0.5 | 0.5 |
92 | 1 | 0.5 | 1 |
93 | 0.867 | 0.4 | 0.65 |
94 | 0 | 0.143 | 0 |
95 | 0 | 0 | 0.143 |
96 | 0.667 | 0.667 | 0.333 |
97 | 1 | 0 | 0.6 |
98 | 1 | 1 | 0.583 |
99 | 0 | 1 | 0 |
100 | 0 | 0 | 0.167 |
101 | 0 | 0 | 0.1 |
102 | 0.688 | 0.5 | 0.375 |
103 | 1 | 0 | 1 |
104 | 0.143 | 1 | 0 |
105 | 1 | 0 | 1 |
106 | 0 | 0 | 0 |
107 | 0.063 | 0 | 1 |
108 | 1 | 0 | 0.333 |
109 | 0.75 | 1 | 0.5 |
110 | 0 | 0 | 0.5 |
111 | 0 | 0 | 0 |
112 | 0 | 0 | 0 |
113 | 0 | 0 | 0 |
114 | 1 | 1 | 1 |
115 | 1 | 0.75 | 0 |
116 | 0 | 0.5 | 0 |
117 | 0 | 0 | 0 |
118 | 1 | 0.333 | 0 |
119 | 0 | 0.1 | 0.2 |
120 | 0 | 0 | 0 |
121 | 0 | 0 | 0 |
122 | 0.143 | 1 | 0 |
123 | 1 | 0 | 0.333 |
124 | 0 | 0 | 0 |
125 | 1 | 0.063 | 1 |
126 | 0 | 0 | 0 |
127 | 0 | 0 | 0 |
128 | 0 | 0 | 0 |
129 | 0.111 | 0 | 1 |
130 | 0 | 0 | 0 |
131 | 0 | 1 | 1 |
132 | 0 | 0 | 0.167 |
133 | 0.5 | 1 | 1 |
134 | 1 | 1 | 1 |
135 | 0.5 | 0 | 0 |
136 | 1 | 0 | 1 |
137 | 0.143 | 0 | 0.143 |
138 | 0.833 | 0 | 0.278 |
139 | 1 | 1 | 1 |
140 | 1 | 0.5 | 0 |
141 | 1 | 1 | 1 |
142 | 0.708 | 0 | 0.165 |
143 | 1 | 0 | 0 |
144 | 1 | 0 | 0 |
145 | 0.333 | 0.333 | 0.333 |
146 | 0 | 0 | 0 |
147 | 0.25 | 0 | 0.5 |
148 | 1 | 0.5 | 1 |
149 | 1 | 1 | 1 |
150 | 0.571 | 0.5 | 0.5 |
151 | 1 | 0 | 0.5 |
152 | 0.444 | 0.111 | 0.333 |
153 | 1 | 0 | 0 |
154 | 0.25 | 0 | 0.5 |
155 | 0 | 0 | 0.2 |
156 | 0.2 | 1 | 0 |
157 | 1 | 1 | 1 |
158 | 0 | 0 | 0 |
159 | 0 | 0 | 0 |
160 | 0 | 0 | 0 |
161 | 0 | 0.25 | 0 |
162 | 0 | 0.333 | 0 |
163 | 0 | 0 | 0 |
164 | 0.333 | 0 | 1 |
165 | 1 | 0 | 0 |
166 | 0 | 0 | 0 |
167 | 0.5 | 1 | 1 |
168 | 1 | 1 | 0.5 |
169 | 1 | 1 | 1 |
170 | 1 | 1 | 0.167 |
171 | 0 | 0 | 0 |
172 | 1 | 1 | 1 |
173 | 1 | 1 | 1 |
174 | 1 | 0.111 | 0 |
175 | 1 | 0 | 0.5 |
176 | 1 | 0 | 0.5 |
177 | 0 | 0 | 0 |
178 | 0.625 | 1 | 0.063 |
179 | 1 | 1 | 1 |
180 | 0.5 | 0 | 0 |
181 | 1 | 1 | 1 |
182 | 1 | 0 | 0 |
183 | 1 | 0 | 1 |
184 | 0 | 0 | 1 |
185 | 0 | 0.125 | 0.333 |
186 | 1 | 0 | 0.5 |
187 | 1 | 0 | 0.333 |
188 | 0 | 0.125 | 0 |
189 | 0 | 0 | 1 |
190 | 1 | 0.5 | 0.417 |
191 | 1 | 0.5 | 0 |
192 | 0 | 0 | 1 |
Task 1c Summary Results by Query
Simple Hit/Miss Counting [5]
MRR Method [6]
Task 2 Results
Task 2 Overall Results
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
Multi Count | 9.275 | 8.744 | 8.744 |
Total Count | 2040 | 2040 | 2040 |
Task 2 Friedman's Test for Significant Differences
The Friedman test was run in MATLAB against the QBSH Task 2 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 |
---|---|---|---|---|---|
JSSLP1 | TY2 | 0.2107 | 0.6250 | 1.0393 | TRUE |
JSSLP1 | TY1 | 0.2107 | 0.6250 | 1.0393 | TRUE |
TY2 | TY1 | -0.4143 | 0.0000 | 0.4143 | FALSE |
Task 2 Summary Results by Query Group
JSSLP1 | TY1 | TY2 | |
---|---|---|---|
1 | 5.889 | 5.333 | 5.333 |
2 | 2.000 | 2.250 | 2.250 |
3 | 4.000 | 0.000 | 0.000 |
4 | 3.200 | 4.400 | 4.400 |
5 | 7.000 | 6.800 | 6.800 |
6 | 3.000 | 5.000 | 5.000 |
7 | 2.800 | 6.600 | 6.600 |
8 | 8.600 | 6.800 | 6.800 |
9 | 3.500 | 2.000 | 2.000 |
10 | 5.889 | 4.556 | 4.556 |
11 | 9.349 | 8.791 | 8.791 |
12 | 9.222 | 9.542 | 9.542 |
13 | 9.145 | 9.237 | 9.237 |
14 | 9.625 | 8.722 | 8.722 |
15 | 8.071 | 5.214 | 5.214 |
16 | 9.757 | 8.932 | 8.932 |
17 | 9.167 | 9.181 | 9.181 |
18 | 9.789 | 9.026 | 9.026 |
19 | 9.478 | 9.333 | 9.333 |
20 | 9.659 | 9.341 | 9.341 |
21 | 8.735 | 8.971 | 8.971 |
22 | 9.663 | 9.398 | 9.398 |
23 | 9.581 | 8.516 | 8.516 |
24 | 8.898 | 9.136 | 9.136 |
25 | 8.898 | 8.119 | 8.119 |
26 | 9.095 | 7.619 | 7.619 |
27 | 9.267 | 8.156 | 8.156 |
28 | 9.152 | 9.043 | 9.043 |
29 | 9.956 | 9.426 | 9.426 |
30 | 9.418 | 9.687 | 9.687 |
31 | 9.800 | 9.650 | 9.650 |
32 | 9.484 | 9.719 | 9.719 |
33 | 8.980 | 7.824 | 7.824 |
34 | 9.721 | 9.456 | 9.456 |
35 | 9.710 | 8.971 | 8.971 |
36 | 7.167 | 7.833 | 7.833 |
37 | 8.893 | 7.250 | 7.250 |
38 | 9.741 | 8.593 | 8.593 |
39 | 9.662 | 7.574 | 7.574 |
40 | 9.481 | 8.815 | 8.815 |
41 | 9.136 | 9.322 | 9.322 |
42 | 9.071 | 7.119 | 7.119 |
43 | 9.483 | 8.483 | 8.483 |
44 | 8.778 | 9.361 | 9.361 |
45 | 9.574 | 7.298 | 7.298 |
46 | 9.368 | 8.368 | 8.368 |
47 | 8.769 | 8.231 | 8.231 |
48 | 8.636 | 8.364 | 8.364 |
Task 2 Summary Results by Query
Run Times
TBA