Difference between revisions of "2007:Symbolic Melodic Similarity Results"

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==Introduction==  
 
==Introduction==  
−
These are the results for the 2007 running of the Symbolic Melodic Similarity task set. For background information about this task set please refer to the [[Symbolic Melodic Similarity]] page.
+
These are the results for the 2007 running of the Symbolic Melodic Similarity task set. For background information about this task set please refer to the [[2007:Symbolic Melodic Similarity]] page.
  
 
Each system was given a query and returned the 10 most melodically similar songs from those taken from the Essen Collection (5274 pieces in the MIDI format; see [http://www.esac-data.org/  ESAC Data Homepage] for more information). For each query, we made four classes of error-mutations, thus the set comprises the following query classes:
 
Each system was given a query and returned the 10 most melodically similar songs from those taken from the Essen Collection (5274 pieces in the MIDI format; see [http://www.esac-data.org/  ESAC Data Homepage] for more information). For each query, we made four classes of error-mutations, thus the set comprises the following query classes:
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'''Total number of unique query/candidate pairs graded''' = 799<br />
 
'''Total number of unique query/candidate pairs graded''' = 799<br />
 
'''Average number of query/candidate pairs evaluated per grader: 133 <br />
 
'''Average number of query/candidate pairs evaluated per grader: 133 <br />
−
'''Number of queries''' = 6 (perfect) with each perfect query error-mutated 4 different ways = 30<br />
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'''Number of queries''' = 5 (perfect) with each perfect query error-mutated 4 different ways = 30<br />
  
 
===General Legend===
 
===General Legend===
 
====Team ID====
 
====Team ID====
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'''FHAR''' = [https://www.music-ir.org/mirex2007/abs/SMS_ferraro.pdf Pascal Ferraro, Pierre Hanna, Julien Allali, Matthias Robine]<br />
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'''FHAR''' = [https://www.music-ir.org/mirex/abstracts/2007/SMS_ferraro.pdf Pascal Ferraro, Pierre Hanna, Julien Allali, Matthias Robine]<br />
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'''GAR1''' = [https://www.music-ir.org/mirex2007/abs/QBSH_SMS_gomez.pdf Carlos G├│mez, Soraya Abad-Mota, Edna Ruckhaus 1]<br />
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'''GAR1''' = [https://www.music-ir.org/mirex/abstracts/2007/QBSH_SMS_gomez.pdf Carlos Gómez begin_of_the_skype_highlighting     end_of_the_skype_highlighting begin_of_the_skype_highlighting     end_of_the_skype_highlighting, Soraya Abad-Mota, Edna Ruckhaus 1]<br />
−
'''GAR2''' = [https://www.music-ir.org/mirex2007/abs/QBSH_SMS_gomez.pdf Carlos G├│mez, Soraya Abad-Mota, Edna Ruckhaus 2]<br />  
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'''GAR2''' = [https://www.music-ir.org/mirex/abstracts/2007/QBSH_SMS_gomez.pdf Carlos Gómez, Soraya Abad-Mota, Edna Ruckhaus 2]<br />  
−
'''AP1''' = [https://www.music-ir.org/mirex2007/abs/SMS_pinto.pdf Alberto Pinto 1]<br />
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'''AP1''' = [https://www.music-ir.org/mirex/abstracts/2007/SMS_pinto.pdf Alberto Pinto 1]<br />
−
'''AP2''' = [https://www.music-ir.org/mirex2007/abs/SMS_pinto.pdf Alberto Pinto 2]<br />
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'''AP2''' = [https://www.music-ir.org/mirex/abstracts/2007/SMS_pinto.pdf Alberto Pinto 2]<br />
−
'''AU1''' = [https://www.music-ir.org/mirex2007/abs/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 1]<br />
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'''AU1''' = [https://www.music-ir.org/mirex/abstracts/2007/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 1]<br />
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'''AU2''' = [https://www.music-ir.org/mirex2007/abs/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 2]<br />
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'''AU2''' = [https://www.music-ir.org/mirex/abstracts/2007/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 2]<br />
−
'''AU3''' = [https://www.music-ir.org/mirex2007/abs/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 3]<br />
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'''AU3''' = [https://www.music-ir.org/mirex/abstracts/2007/QBSH_SMS_uitdenbogerd.pdf Alexandra L. Uitdenbogerd 3]<br />
  
 
====Broad Categories====
 
====Broad Categories====
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==Summary Results==
 
==Summary Results==
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==Run Times==
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===Run Times===
−
<csv>sms_runtimes.csv</csv>
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<csv>2007/sms_runtimes.csv</csv>
 
===Overall Scores (Includes Perfect and Error Candidates)===
 
===Overall Scores (Includes Perfect and Error Candidates)===
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<csv>SMS07_overall_norm.csv</csv>
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<csv>2007/SMS07_overall_norm.csv</csv>
  
 
===Overall Summaries (Presented by Error Types)===
 
===Overall Summaries (Presented by Error Types)===
  
−
<csv>SMS07_errors_norm.csv</csv>
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<csv>2007/SMS07_errors_norm.csv</csv>
−
 
 
 
===Friedman Test with Multiple Comparisons Results (p=0.05)===
 
===Friedman Test with Multiple Comparisons Results (p=0.05)===
−
The Friedman test was run in MATLAB against the Fine summary data over the 100 queries.<br />
+
The Friedman test was run in MATLAB against the Fine summary data over the 30 queries.<br />
 
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>sms07_sum_friedman_fine.csv</csv>
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<csv>2007/sms07_sum_friedman_fine.csv</csv>
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<csv>sms07_detail_friedman_fine.csv</csv>
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<csv>2007/sms07_detail_friedman_fine.csv</csv>
  
 
[[Image:2007 sms fine scores friedmans.png]]
 
[[Image:2007 sms fine scores friedmans.png]]
  
 
==Raw Scores==
 
==Raw Scores==
−
The raw data derived from the Evalutron 6000 human evaluations are located on the [[Symbolic Melodic Similarity Raw Data]] page.
+
The raw data derived from the Evalutron 6000 human evaluations are located on the 2007:Symbolic Melodic Similarity Raw Data page.
  
 
[[Category: Results]]
 
[[Category: Results]]

Latest revision as of 22:12, 19 December 2011

Introduction

These are the results for the 2007 running of the Symbolic Melodic Similarity task set. For background information about this task set please refer to the 2007:Symbolic Melodic Similarity page.

Each system was given a query and returned the 10 most melodically similar songs from those taken from the Essen Collection (5274 pieces in the MIDI format; see ESAC Data Homepage for more information). For each query, we made four classes of error-mutations, thus the set comprises the following query classes:

  • 0. No errors
  • 1. One note deleted
  • 2. One note inserted
  • 3. One interval enlarged
  • 4. One interval compressed

For each query (and its 4 mutations), the returned results (candidates) from all systems were then grouped together (query set) for evaluation by the human graders. The graders were provide with only heard perfect version against which to evaluate the candidates and did not know whether the candidates came from a perfect or mutated query. Each query/candidate set was evaluated by 1 individual grader. Using the Evalutron 6000 system, the graders gave each query/candidate pair two types of scores. Graders were asked to provide 1 categorical score with 3 categories: NS,SS,VS as explained below, and one fine score (in the range from 0 to 10).

Evalutron 6000 Summary Data

Number of evaluators = 6
Number of evaluations per query/candidate pair = 1
Number of queries per grader = 1
Total number of candidates returned = 2400
Total number of unique query/candidate pairs graded = 799
Average number of query/candidate pairs evaluated per grader: 133
Number of queries = 5 (perfect) with each perfect query error-mutated 4 different ways = 30

General Legend

Team ID

FHAR = Pascal Ferraro, Pierre Hanna, Julien Allali, Matthias Robine
GAR1 = Carlos Gómez begin_of_the_skype_highlighting     end_of_the_skype_highlighting begin_of_the_skype_highlighting     end_of_the_skype_highlighting, Soraya Abad-Mota, Edna Ruckhaus 1
GAR2 = Carlos Gómez, Soraya Abad-Mota, Edna Ruckhaus 2
AP1 = Alberto Pinto 1
AP2 = Alberto Pinto 2
AU1 = Alexandra L. Uitdenbogerd 1
AU2 = Alexandra L. Uitdenbogerd 2
AU3 = Alexandra L. Uitdenbogerd 3

Broad Categories

NS = Not Similar
SS = Somewhat Similar
VS = Very Similar

Table Headings (Other metrics to be added soon to results by Xiao Hu )

ADR = Average Dynamic Recall
NRGB = Normalize Recall at Group Boundaries
AP = Average Precision (non-interpolated)
PND = Precision at N Documents

Calculating Summary Measures

Fine(1) = Sum of fine-grained human similarity decisions (0-10).
PSum(1) = Sum of human broad similarity decisions: NS=0, SS=1, VS=2.
WCsum(1) = 'World Cup' scoring: NS=0, SS=1, VS=3 (rewards Very Similar).
SDsum(1) = 'Stephen Downie' scoring: NS=0, SS=1, VS=4 (strongly rewards Very Similar).
Greater0(1) = NS=0, SS=1, VS=1 (binary relevance judgement).
Greater1(1) = NS=0, SS=0, VS=1 (binary relevance judgement using only Very Similar).

(1)Normalized to the range 0 to 1.

Summary Results

Run Times

Participant Runtime (sec)
AP1 query:
AP2 query:
AU indexing: 7
AU1 query: 9
AU2 query: 63
AU3 query: 11
GAR indexing: 5
GAR1 query: 5143
GAR2 query: 4728
FHAR query: 1155

download these results as csv

Overall Scores (Includes Perfect and Error Candidates)

Overall AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.031 0.024 0.666 0.698 0.706 0.712 0.739 0.730
NRGB 0.028 0.027 0.601 0.590 0.616 0.617 0.683 0.666
AP 0.017 0.023 0.525 0.477 0.500 0.508 0.545 0.545
PND 0.044 0.056 0.527 0.495 0.515 0.494 0.588 0.557
Fine 0.292 0.281 0.532 0.528 0.532 0.586 0.581 0.540
Psum 0.234 0.190 0.522 0.524 0.527 0.589 0.580 0.517
WCsum 0.179 0.146 0.470 0.480 0.486 0.537 0.526 0.470
SDsum 0.152 0.123 0.444 0.458 0.465 0.511 0.498 0.447
Greater0 0.397 0.323 0.677 0.653 0.650 0.743 0.743 0.657
Greater1 0.070 0.057 0.367 0.393 0.403 0.433 0.417 0.377

download these results as csv

Overall Summaries (Presented by Error Types)

No Errors AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.009 0.024 0.687 0.692 0.694 0.707 0.730 0.743
NRGB 0.010 0.030 0.615 0.576 0.583 0.602 0.675 0.661
AP 0.008 0.029 0.539 0.461 0.477 0.499 0.528 0.551
PND 0.021 0.069 0.556 0.491 0.488 0.496 0.561 0.548
Fine 0.260 0.283 0.555 0.542 0.539 0.581 0.582 0.527
Psum 0.183 0.208 0.550 0.542 0.525 0.592 0.600 0.500
WCsum 0.139 0.161 0.494 0.500 0.500 0.544 0.544 0.450
SDsum 0.117 0.138 0.467 0.479 0.488 0.521 0.517 0.425
Greater0 0.317 0.350 0.717 0.667 0.600 0.733 0.767 0.650
Greater1 0.050 0.067 0.383 0.417 0.450 0.450 0.433 0.350
Deleted AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.062 0.003 0.673 0.730 0.761 0.704 0.762 0.743
NRGB 0.058 0.003 0.663 0.656 0.740 0.628 0.720 0.706
AP 0.036 0.012 0.557 0.532 0.613 0.531 0.598 0.571
PND 0.085 0.024 0.556 0.568 0.646 0.509 0.640 0.574
Fine 0.343 0.258 0.518 0.529 0.511 0.580 0.578 0.554
Psum 0.317 0.175 0.500 0.525 0.508 0.592 0.575 0.542
Wcsum 0.256 0.128 0.450 0.478 0.461 0.533 0.522 0.489
SDsum 0.225 0.104 0.425 0.454 0.438 0.504 0.496 0.463
Greater0 0.500 0.317 0.650 0.667 0.650 0.767 0.733 0.700
Greater1 0.133 0.033 0.350 0.383 0.367 0.417 0.417 0.383
Inserted AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.037 0.047 0.677 0.685 0.685 0.718 0.736 0.699
NRGB 0.036 0.047 0.610 0.555 0.573 0.608 0.668 0.667
AP 0.013 0.030 0.548 0.469 0.470 0.494 0.530 0.537
PND 0.056 0.052 0.572 0.456 0.483 0.474 0.557 0.563
Fine 0.310 0.278 0.501 0.505 0.505 0.591 0.568 0.518
Psum 0.258 0.175 0.467 0.492 0.500 0.592 0.542 0.492
WCsum 0.194 0.133 0.417 0.450 0.456 0.539 0.494 0.450
SDsum 0.163 0.113 0.392 0.429 0.433 0.513 0.471 0.429
Greater0 0.450 0.300 0.617 0.617 0.633 0.750 0.683 0.617
Greater1 0.067 0.050 0.317 0.367 0.367 0.433 0.400 0.367
Enlarged AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.034 0.036 0.668 0.716 0.704 0.722 0.732 0.710
NRGB 0.017 0.037 0.589 0.604 0.604 0.631 0.665 0.624
AP 0.019 0.026 0.478 0.462 0.465 0.503 0.526 0.511
PND 0.019 0.085 0.483 0.474 0.474 0.516 0.576 0.525
Fine 0.255 0.300 0.531 0.520 0.558 0.595 0.589 0.535
Psum 0.167 0.200 0.533 0.517 0.558 0.583 0.592 0.500
WCsum 0.133 0.161 0.483 0.472 0.511 0.533 0.533 0.456
SDsum 0.117 0.142 0.458 0.450 0.488 0.508 0.504 0.433
Greater0 0.267 0.317 0.683 0.650 0.700 0.733 0.767 0.633
Greater1 0.067 0.083 0.383 0.383 0.417 0.433 0.417 0.367
Compressed AP1 AP2 AU1 AU2 AU3 GAR1 GAR2 FHAR
ADR 0.012 0.011 0.627 0.667 0.686 0.710 0.737 0.757
NRGB 0.017 0.019 0.526 0.561 0.578 0.618 0.688 0.673
AP 0.007 0.016 0.504 0.459 0.475 0.515 0.545 0.553
PND 0.037 0.048 0.468 0.487 0.482 0.475 0.605 0.574
Fine 0.290 0.287 0.554 0.546 0.549 0.584 0.589 0.567
Psum 0.242 0.192 0.558 0.542 0.542 0.583 0.592 0.550
WCsum 0.172 0.144 0.506 0.500 0.500 0.533 0.533 0.506
SDsum 0.138 0.121 0.479 0.479 0.479 0.508 0.504 0.483
Greater0 0.450 0.333 0.717 0.667 0.667 0.733 0.767 0.683
Greater1 0.033 0.050 0.400 0.417 0.417 0.433 0.417 0.417

download these results as csv

Friedman Test with Multiple Comparisons Results (p=0.05)

The Friedman test was run in MATLAB against the Fine summary data over the 30 queries.
Command: [c,m,h,gnames] = multcompare(stats, 'ctype', 'tukey-kramer','estimate', 'friedman', 'alpha', 0.05);

Friedman's ANOVA Table
Source SS df MS Chi-sq Prob>Chi-sq
Columns 727.3833 7 103.9119 121.2787 0
Error 532.1167 203 2.6213
Total 1.2595e+003 239

download these results as csv

TeamID TeamID Lowerbound Mean Upperbound Significance
AP1 AP2 -1.9498 -0.0333 1.8832 FALSE
AP1 AU1 -5.5832 -3.6667 -1.7502 TRUE
AP1 AU2 -5.2665 -3.35 -1.4335 TRUE
AP1 AU3 -5.3332 -3.4167 -1.5002 TRUE
AP1 CG1 -6.6832 -4.7667 -2.8502 TRUE
AP1 CG2 -6.2832 -4.3667 -2.4502 TRUE
AP1 FH -5.7832 -3.8667 -1.9502 TRUE
AP2 AU1 -5.5498 -3.6333 -1.7168 TRUE
AP2 AU2 -5.2332 -3.3167 -1.4002 TRUE
AP2 AU3 -5.2998 -3.3833 -1.4668 TRUE
AP2 CG1 -6.6498 -4.7333 -2.8168 TRUE
AP2 CG2 -6.2498 -4.3333 -2.4168 TRUE
AP2 FH -5.7498 -3.8333 -1.9168 TRUE
AU1 AU2 -1.5998 0.3167 2.2332 FALSE
AU1 AU3 -1.6665 0.25 2.1665 FALSE
AU1 CG1 -3.0165 -1.1 0.8165 FALSE
AU1 CG2 -2.6165 -0.7 1.2165 FALSE
AU1 FH -2.1165 -0.2 1.7165 FALSE
AU2 AU3 -1.9832 -0.0667 1.8498 FALSE
AU2 CG1 -3.3332 -1.4167 0.4998 FALSE
AU2 CG2 -2.9332 -1.0167 0.8998 FALSE
AU2 FH -2.4332 -0.5167 1.3998 FALSE
AU3 CG1 -3.2665 -1.35 0.5665 FALSE
AU3 CG2 -2.8665 -0.95 0.9665 FALSE
AU3 FH -2.3665 -0.45 1.4665 FALSE
CG1 CG2 -1.5165 0.4 2.3165 FALSE
CG1 FH -1.0165 0.9 2.8165 FALSE
CG2 FH -1.4165 0.5 2.4165 FALSE

download these results as csv

2007 sms fine scores friedmans.png

Raw Scores

The raw data derived from the Evalutron 6000 human evaluations are located on the 2007:Symbolic Melodic Similarity Raw Data page.

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