rwc_mdb_p

This is the tempo_eval report for the ‘rwc_mdb_p’ corpus.

Reports for other corpora may be found here.

Table of Contents

References for ‘rwc_mdb_p’

References

0.1

Attribute Value
Corpus rwc_mdb_p
Version 0.1
Curator Masataka Goto
Data Source AIST website. Tempo values are rough estimates and should not be used as data for research purposes.
Annotation Tools unknown
Annotation Rules unknown
Annotator, name Masataka Goto
Annotator, bibtex Goto2006
Annotator, ref_url https://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/

1.0

Attribute Value
Corpus rwc_mdb_p
Version 1.0
Curator Masataka Goto
Data Source manual annotation
Annotation Tools derived from beat annotations
Annotation Rules median of corresponding inter beat intervals
Annotator, name Masataka Goto
Annotator, bibtex Goto2006
Annotator, ref_url https://staff.aist.go.jp/m.goto/RWC-MDB/AIST-Annotation/

Basic Statistics

Reference Size Min Max Avg Stdev Sweet Oct. Start Sweet Oct. Coverage
0.1 100 62.00 200.00 110.81 27.66 70.00 0.88
1.0 100 62.02 200.00 111.68 27.51 69.00 0.87

Table 1: Basic statistics.

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Smoothed Tempo Distribution

Figure 1: Percentage of values in tempo interval.

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Tag Distribution for ‘tag_open’

Figure 2: Percentage of tracks tagged with tags from namespace ‘tag_open’. Annotations are from reference 1.0.

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Beat-Based Tempo Variation

Figure 3: Fraction of the dataset with beat-annotated tracks with cvar < τ.

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Estimates for ‘rwc_mdb_p’

Estimators

boeck2015/tempodetector2016_default

Attribute Value
Corpus rwc_mdb_p
Version 0.17.dev0
Annotation Tools TempoDetector.2016, madmom, https://github.com/CPJKU/madmom
Annotator, bibtex Boeck2015

davies2009/mirex_qm_tempotracker

Attribute Value  
Corpus rwc_mdb_p  
Version 1.0  
Annotation Tools QM Tempotracker, Sonic Annotator plugin. https://code.soundsoftware.ac.uk/projects/mirex2013/repository/show/audio_tempo_estimation/qm-tempotracker Note that the current macOS build of ‘qm-vamp-plugins’ was used.  
Annotator, bibtex Davies2009 Davies2007

percival2014/stem

Attribute Value
Corpus rwc_mdb_p
Version 1.0
Annotation Tools percival 2014, ‘tempo’ implementation from Marsyas, http://marsyas.info, git checkout tempo-stem
Annotator, bibtex Percival2014

schreiber2014/default

Attribute Value
Corpus rwc_mdb_p
Version 0.0.1
Annotation Tools schreiber 2014, http://www.tagtraum.com/tempo_estimation.html
Annotator, bibtex Schreiber2014

schreiber2017/ismir2017

Attribute Value
Corpus rwc_mdb_p
Version 0.0.4
Annotation Tools schreiber 2017, model=ismir2017, http://www.tagtraum.com/tempo_estimation.html
Annotator, bibtex Schreiber2017

schreiber2017/mirex2017

Attribute Value
Corpus rwc_mdb_p
Version 0.0.4
Annotation Tools schreiber 2017, model=mirex2017, http://www.tagtraum.com/tempo_estimation.html
Annotator, bibtex Schreiber2017

schreiber2018/cnn

Attribute Value
Corpus  
Version 0.0.3
Data Source Hendrik Schreiber, Meinard Müller. A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.
Annotation Tools schreiber tempo-cnn (model=cnn), https://github.com/hendriks73/tempo-cnn

schreiber2018/fcn

Attribute Value
Corpus  
Version 0.0.3
Data Source Hendrik Schreiber, Meinard Müller. A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.
Annotation Tools schreiber tempo-cnn (model=fcn), https://github.com/hendriks73/tempo-cnn

schreiber2018/ismir2018

Attribute Value
Corpus  
Version 0.0.3
Data Source Hendrik Schreiber, Meinard Müller. A Single-Step Approach to Musical Tempo Estimation Using a Convolutional Neural Network. In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR), Paris, France, Sept. 2018.
Annotation Tools schreiber tempo-cnn (model=ismir2018), https://github.com/hendriks73/tempo-cnn

Basic Statistics

Estimator Size Min Max Avg Stdev Sweet Oct. Start Sweet Oct. Coverage
boeck2015/tempodetector2016_default 100 41.96 187.50 90.99 31.62 54.00 0.67
davies2009/mirex_qm_tempotracker 100 80.75 178.21 123.00 24.28 87.00 0.95
percival2014/stem 100 60.09 148.72 102.91 22.33 70.00 0.94
schreiber2014/default 100 59.93 157.99 101.33 22.55 68.00 0.92
schreiber2017/ismir2017 100 54.00 163.00 106.23 24.79 69.00 0.90
schreiber2017/mirex2017 100 60.01 163.00 107.34 23.65 69.00 0.92
schreiber2018/cnn 100 60.00 188.00 113.65 29.37 70.00 0.85
schreiber2018/fcn 100 54.00 197.00 109.32 29.51 70.00 0.87
schreiber2018/ismir2018 100 70.00 188.00 110.28 24.17 71.00 0.92

Table 2: Basic statistics.

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Smoothed Tempo Distribution

Figure 4: Percentage of values in tempo interval.

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Accuracy

Accuracy1 is defined as the percentage of correct estimates, allowing a 4% tolerance for individual BPM values.

Accuracy2 additionally permits estimates to be wrong by a factor of 2, 3, 1/2 or 1/3 (so-called octave errors).

See [Gouyon2006].

Note: When comparing accuracy values for different algorithms, keep in mind that an algorithm may have been trained on the test set or that the test set may have even been created using one of the tested algorithms.

Accuracy Results for 0.1

Estimator Accuracy1 Accuracy2
schreiber2018/cnn 0.9000 0.9900
schreiber2017/mirex2017 0.8900 0.9900
schreiber2018/ismir2018 0.8700 0.9900
schreiber2017/ismir2017 0.8700 0.9900
schreiber2018/fcn 0.8600 0.9900
percival2014/stem 0.8500 0.9900
schreiber2014/default 0.8200 0.9500
davies2009/mirex_qm_tempotracker 0.7500 0.9600
boeck2015/tempodetector2016_default 0.6500 0.9800

Table 3: Mean accuracy of estimates compared to version 0.1 with 4% tolerance ordered by Accuracy1.

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Raw data Accuracy1: CSV JSON LATEX PICKLE

Raw data Accuracy2: CSV JSON LATEX PICKLE

Accuracy1 for 0.1

Figure 5: Mean Accuracy1 for estimates compared to version 0.1 depending on tolerance.

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Accuracy2 for 0.1

Figure 6: Mean Accuracy2 for estimates compared to version 0.1 depending on tolerance.

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Accuracy Results for 1.0

Estimator Accuracy1 Accuracy2
schreiber2018/cnn 0.9000 1.0000
schreiber2018/ismir2018 0.8900 1.0000
schreiber2017/mirex2017 0.8900 1.0000
schreiber2017/ismir2017 0.8700 1.0000
schreiber2018/fcn 0.8600 1.0000
percival2014/stem 0.8500 1.0000
schreiber2014/default 0.8200 0.9600
davies2009/mirex_qm_tempotracker 0.7900 0.9900
boeck2015/tempodetector2016_default 0.6500 1.0000

Table 4: Mean accuracy of estimates compared to version 1.0 with 4% tolerance ordered by Accuracy1.

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Raw data Accuracy1: CSV JSON LATEX PICKLE

Raw data Accuracy2: CSV JSON LATEX PICKLE

Accuracy1 for 1.0

Figure 7: Mean Accuracy1 for estimates compared to version 1.0 depending on tolerance.

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Accuracy2 for 1.0

Figure 8: Mean Accuracy2 for estimates compared to version 1.0 depending on tolerance.

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Differing Items

For which items did a given estimator not estimate a correct value with respect to a given ground truth? Are there items which are either very difficult, not suitable for the task, or incorrectly annotated and therefore never estimated correctly, regardless which estimator is used?

Differing Items Accuracy1

Items with different tempo annotations (Accuracy1, 4% tolerance) in different versions:

0.1 compared with boeck2015/tempodetector2016_default (35 differences): ‘RM-P001’ ‘RM-P003’ ‘RM-P005’ ‘RM-P010’ ‘RM-P012’ ‘RM-P015’ ‘RM-P018’ ‘RM-P020’ ‘RM-P022’ ‘RM-P023’ ‘RM-P027’ … CSV

0.1 compared with davies2009/mirex_qm_tempotracker (25 differences): ‘RM-P004’ ‘RM-P009’ ‘RM-P037’ ‘RM-P039’ ‘RM-P041’ ‘RM-P045’ ‘RM-P046’ ‘RM-P055’ ‘RM-P056’ ‘RM-P057’ ‘RM-P060’ … CSV

0.1 compared with percival2014/stem (15 differences): ‘RM-P009’ ‘RM-P026’ ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P043’ ‘RM-P046’ ‘RM-P053’ ‘RM-P060’ ‘RM-P069’ ‘RM-P072’ … CSV

0.1 compared with schreiber2014/default (18 differences): ‘RM-P001’ ‘RM-P009’ ‘RM-P031’ ‘RM-P035’ ‘RM-P037’ ‘RM-P038’ ‘RM-P041’ ‘RM-P043’ ‘RM-P046’ ‘RM-P053’ ‘RM-P057’ … CSV

0.1 compared with schreiber2017/ismir2017 (13 differences): ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P056’ ‘RM-P057’ ‘RM-P062’ ‘RM-P072’ ‘RM-P073’ ‘RM-P075’ ‘RM-P077’ … CSV

0.1 compared with schreiber2017/mirex2017 (11 differences): ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P057’ ‘RM-P062’ ‘RM-P069’ ‘RM-P072’ ‘RM-P073’ ‘RM-P077’ ‘RM-P095’ … CSV

0.1 compared with schreiber2018/cnn (10 differences): ‘RM-P004’ ‘RM-P034’ ‘RM-P041’ ‘RM-P048’ ‘RM-P056’ ‘RM-P069’ ‘RM-P072’ ‘RM-P077’ ‘RM-P083’ ‘RM-P097’ CSV

0.1 compared with schreiber2018/fcn (14 differences): ‘RM-P004’ ‘RM-P026’ ‘RM-P027’ ‘RM-P041’ ‘RM-P048’ ‘RM-P056’ ‘RM-P060’ ‘RM-P069’ ‘RM-P072’ ‘RM-P073’ ‘RM-P075’ … CSV

0.1 compared with schreiber2018/ismir2018 (13 differences): ‘RM-P009’ ‘RM-P026’ ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P056’ ‘RM-P062’ ‘RM-P069’ ‘RM-P071’ ‘RM-P072’ … CSV

1.0 compared with boeck2015/tempodetector2016_default (35 differences): ‘RM-P001’ ‘RM-P003’ ‘RM-P005’ ‘RM-P010’ ‘RM-P012’ ‘RM-P015’ ‘RM-P018’ ‘RM-P020’ ‘RM-P022’ ‘RM-P023’ ‘RM-P027’ … CSV

1.0 compared with davies2009/mirex_qm_tempotracker (21 differences): ‘RM-P004’ ‘RM-P009’ ‘RM-P037’ ‘RM-P039’ ‘RM-P041’ ‘RM-P045’ ‘RM-P046’ ‘RM-P055’ ‘RM-P056’ ‘RM-P057’ ‘RM-P060’ … CSV

1.0 compared with percival2014/stem (15 differences): ‘RM-P009’ ‘RM-P026’ ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P043’ ‘RM-P046’ ‘RM-P053’ ‘RM-P060’ ‘RM-P069’ ‘RM-P071’ … CSV

1.0 compared with schreiber2014/default (18 differences): ‘RM-P001’ ‘RM-P009’ ‘RM-P031’ ‘RM-P035’ ‘RM-P037’ ‘RM-P038’ ‘RM-P041’ ‘RM-P043’ ‘RM-P046’ ‘RM-P053’ ‘RM-P057’ … CSV

1.0 compared with schreiber2017/ismir2017 (13 differences): ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P056’ ‘RM-P057’ ‘RM-P062’ ‘RM-P071’ ‘RM-P073’ ‘RM-P075’ ‘RM-P077’ … CSV

1.0 compared with schreiber2017/mirex2017 (11 differences): ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P057’ ‘RM-P062’ ‘RM-P069’ ‘RM-P071’ ‘RM-P073’ ‘RM-P077’ ‘RM-P095’ … CSV

1.0 compared with schreiber2018/cnn (10 differences): ‘RM-P004’ ‘RM-P034’ ‘RM-P041’ ‘RM-P048’ ‘RM-P056’ ‘RM-P069’ ‘RM-P071’ ‘RM-P077’ ‘RM-P083’ ‘RM-P097’ CSV

1.0 compared with schreiber2018/fcn (14 differences): ‘RM-P004’ ‘RM-P026’ ‘RM-P027’ ‘RM-P041’ ‘RM-P048’ ‘RM-P056’ ‘RM-P060’ ‘RM-P069’ ‘RM-P071’ ‘RM-P073’ ‘RM-P075’ … CSV

1.0 compared with schreiber2018/ismir2018 (11 differences): ‘RM-P009’ ‘RM-P026’ ‘RM-P035’ ‘RM-P037’ ‘RM-P041’ ‘RM-P046’ ‘RM-P056’ ‘RM-P062’ ‘RM-P069’ ‘RM-P095’ ‘RM-P097’ … CSV

None of the estimators estimated the following item ‘correctly’ using Accuracy1: ‘RM-P041’ CSV

Differing Items Accuracy2

Items with different tempo annotations (Accuracy2, 4% tolerance) in different versions:

0.1 compared with boeck2015/tempodetector2016_default (2 differences): ‘RM-P072’ ‘RM-P073’ CSV

0.1 compared with davies2009/mirex_qm_tempotracker (4 differences): ‘RM-P060’ ‘RM-P072’ ‘RM-P073’ ‘RM-P097’ CSV

0.1 compared with percival2014/stem (1 differences): ‘RM-P072’ CSV

0.1 compared with schreiber2014/default (5 differences): ‘RM-P009’ ‘RM-P038’ ‘RM-P053’ ‘RM-P057’ ‘RM-P072’ CSV

0.1 compared with schreiber2017/ismir2017 (1 differences): ‘RM-P072’ CSV

0.1 compared with schreiber2017/mirex2017 (1 differences): ‘RM-P072’ CSV

0.1 compared with schreiber2018/cnn (1 differences): ‘RM-P072’ CSV

0.1 compared with schreiber2018/fcn (1 differences): ‘RM-P072’ CSV

0.1 compared with schreiber2018/ismir2018 (1 differences): ‘RM-P072’ CSV

1.0 compared with boeck2015/tempodetector2016_default: No differences.

1.0 compared with davies2009/mirex_qm_tempotracker (1 differences): ‘RM-P060’ CSV

1.0 compared with percival2014/stem: No differences.

1.0 compared with schreiber2014/default (4 differences): ‘RM-P009’ ‘RM-P038’ ‘RM-P053’ ‘RM-P057’ CSV

1.0 compared with schreiber2017/ismir2017: No differences.

1.0 compared with schreiber2017/mirex2017: No differences.

1.0 compared with schreiber2018/cnn: No differences.

1.0 compared with schreiber2018/fcn: No differences.

1.0 compared with schreiber2018/ismir2018: No differences.

All tracks were estimated ‘correctly’ by at least one system.

Significance of Differences

Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0649 0.0012 0.0033 0.0002 0.0000 0.0000 0.0005 0.0001
davies2009/mirex_qm_tempotracker 0.0649 1.0000 0.3075 0.7111 0.1338 0.0414 0.0347 0.2295 0.0309
percival2014/stem 0.0012 0.3075 1.0000 0.5488 0.7744 0.3437 0.3018 1.0000 0.3437
schreiber2014/default 0.0033 0.7111 0.5488 1.0000 0.2266 0.0654 0.1153 0.5235 0.1435
schreiber2017/ismir2017 0.0002 0.1338 0.7744 0.2266 1.0000 0.6250 0.5811 1.0000 0.7539
schreiber2017/mirex2017 0.0000 0.0414 0.3437 0.0654 0.6250 1.0000 1.0000 0.6072 1.0000
schreiber2018/cnn 0.0000 0.0347 0.3018 0.1153 0.5811 1.0000 1.0000 0.2891 1.0000
schreiber2018/fcn 0.0005 0.2295 1.0000 0.5235 1.0000 0.6072 0.2891 1.0000 0.6291
schreiber2018/ismir2018 0.0001 0.0309 0.3437 0.1435 0.7539 1.0000 1.0000 0.6291 1.0000

Table 5: McNemar p-values, using reference annotations 1.0 as groundtruth with Accuracy1 [Gouyon2006]. H0: both estimators disagree with the groundtruth to the same amount. If p<=ɑ, reject H0, i.e. we have a significant difference in the disagreement with the groundtruth. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.1934 0.0012 0.0033 0.0002 0.0000 0.0000 0.0005 0.0005
davies2009/mirex_qm_tempotracker 0.1934 1.0000 0.0755 0.2649 0.0169 0.0026 0.0026 0.0433 0.0075
percival2014/stem 0.0012 0.0755 1.0000 0.5488 0.7744 0.3437 0.3018 1.0000 0.7539
schreiber2014/default 0.0033 0.2649 0.5488 1.0000 0.2266 0.0654 0.1153 0.5235 0.3323
schreiber2017/ismir2017 0.0002 0.0169 0.7744 0.2266 1.0000 0.6250 0.5811 1.0000 1.0000
schreiber2017/mirex2017 0.0000 0.0026 0.3437 0.0654 0.6250 1.0000 1.0000 0.6072 0.7266
schreiber2018/cnn 0.0000 0.0026 0.3018 0.1153 0.5811 1.0000 1.0000 0.2891 0.5811
schreiber2018/fcn 0.0005 0.0433 1.0000 0.5235 1.0000 0.6072 0.2891 1.0000 1.0000
schreiber2018/ismir2018 0.0005 0.0075 0.7539 0.3323 1.0000 0.7266 0.5811 1.0000 1.0000

Table 6: McNemar p-values, using reference annotations 0.1 as groundtruth with Accuracy1 [Gouyon2006]. H0: both estimators disagree with the groundtruth to the same amount. If p<=ɑ, reject H0, i.e. we have a significant difference in the disagreement with the groundtruth. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
davies2009/mirex_qm_tempotracker 1.0000 1.0000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000
percival2014/stem 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2014/default 0.1250 0.3750 0.1250 1.0000 0.1250 0.1250 0.1250 0.1250 0.1250
schreiber2017/ismir2017 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2017/mirex2017 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/cnn 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/fcn 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/ismir2018 1.0000 1.0000 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000

Table 7: McNemar p-values, using reference annotations 1.0 as groundtruth with Accuracy2 [Gouyon2006]. H0: both estimators disagree with the groundtruth to the same amount. If p<=ɑ, reject H0, i.e. we have a significant difference in the disagreement with the groundtruth. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.5000 1.0000 0.3750 1.0000 1.0000 1.0000 1.0000 1.0000
davies2009/mirex_qm_tempotracker 0.5000 1.0000 0.2500 1.0000 0.2500 0.2500 0.2500 0.2500 0.2500
percival2014/stem 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2014/default 0.3750 1.0000 0.1250 1.0000 0.1250 0.1250 0.1250 0.1250 0.1250
schreiber2017/ismir2017 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2017/mirex2017 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/cnn 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/fcn 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000
schreiber2018/ismir2018 1.0000 0.2500 1.0000 0.1250 1.0000 1.0000 1.0000 1.0000 1.0000

Table 8: McNemar p-values, using reference annotations 0.1 as groundtruth with Accuracy2 [Gouyon2006]. H0: both estimators disagree with the groundtruth to the same amount. If p<=ɑ, reject H0, i.e. we have a significant difference in the disagreement with the groundtruth. In the table, p-values<0.05 are set in bold.

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Accuracy1 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

Accuracy1 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 9: Mean Accuracy1 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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Accuracy1 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 10: Mean Accuracy1 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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Accuracy2 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

Accuracy2 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 11: Mean Accuracy2 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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Accuracy2 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 12: Mean Accuracy2 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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Accuracy1 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean Accuracy1 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

Accuracy1 on Tempo-Subsets for 0.1

Figure 13: Mean Accuracy1 for estimates compared to version 0.1 for tempo intervals around T.

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Accuracy1 on Tempo-Subsets for 1.0

Figure 14: Mean Accuracy1 for estimates compared to version 1.0 for tempo intervals around T.

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Accuracy2 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean Accuracy2 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

Accuracy2 on Tempo-Subsets for 0.1

Figure 15: Mean Accuracy2 for estimates compared to version 0.1 for tempo intervals around T.

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Accuracy2 on Tempo-Subsets for 1.0

Figure 16: Mean Accuracy2 for estimates compared to version 1.0 for tempo intervals around T.

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Estimated Accuracy1 for Tempo

When fitting a generalized additive model (GAM) to Accuracy1-values and a ground truth, what Accuracy1 can we expect with confidence?

Estimated Accuracy1 for Tempo for 0.1

Predictions of GAMs trained on Accuracy1 for estimates for reference 0.1.

Figure 17: Accuracy1 predictions of a generalized additive model (GAM) fit to Accuracy1 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated Accuracy1 for Tempo for 1.0

Predictions of GAMs trained on Accuracy1 for estimates for reference 1.0.

Figure 18: Accuracy1 predictions of a generalized additive model (GAM) fit to Accuracy1 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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Estimated Accuracy2 for Tempo

When fitting a generalized additive model (GAM) to Accuracy2-values and a ground truth, what Accuracy2 can we expect with confidence?

Estimated Accuracy2 for Tempo for 0.1

Predictions of GAMs trained on Accuracy2 for estimates for reference 0.1.

Figure 19: Accuracy2 predictions of a generalized additive model (GAM) fit to Accuracy2 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated Accuracy2 for Tempo for 1.0

Predictions of GAMs trained on Accuracy2 for estimates for reference 1.0.

Figure 20: Accuracy2 predictions of a generalized additive model (GAM) fit to Accuracy2 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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Accuracy1 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

Accuracy1 for ‘tag_open’ Tags for 0.1

Figure 21: Mean Accuracy1 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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Accuracy1 for ‘tag_open’ Tags for 1.0

Figure 22: Mean Accuracy1 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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Accuracy2 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

Accuracy2 for ‘tag_open’ Tags for 0.1

Figure 23: Mean Accuracy2 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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Accuracy2 for ‘tag_open’ Tags for 1.0

Figure 24: Mean Accuracy2 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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OE1 and OE2

OE1 is defined as octave error between an estimate E and a reference value R.This means that the most common errors—by a factor of 2 or ½—have the same magnitude, namely 1: OE2(E) = log2(E/R).

OE2 is the signed OE1 corresponding to the minimum absolute OE1 allowing the octaveerrors 2, 3, 1/2, and 1/3: OE2(E) = arg minx(|x|) with x ∈ {OE1(E), OE1(2E), OE1(3E), OE1(½E), OE1(⅓E)}

Mean OE1/OE2 Results for 0.1

Estimator OE1_MEAN OE1_STDEV OE2_MEAN OE2_STDEV
schreiber2018/cnn 0.0328 0.3003 0.0028 0.0170
schreiber2017/mirex2017 -0.0375 0.3121 0.0025 0.0153
schreiber2017/ismir2017 -0.0575 0.3401 0.0025 0.0153
schreiber2018/ismir2018 0.0026 0.3477 0.0026 0.0170
schreiber2014/default -0.1211 0.3563 -0.0111 0.0715
schreiber2018/fcn -0.0274 0.3593 0.0026 0.0171
percival2014/stem -0.0973 0.3610 0.0027 0.0164
davies2009/mirex_qm_tempotracker 0.1665 0.4421 0.0265 0.0442
boeck2015/tempodetector2016_default -0.3235 0.5013 0.0024 0.0171

Table 9: Mean OE1/OE2 for estimates compared to version 0.1 ordered by standard deviation.

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Raw data OE1: CSV JSON LATEX PICKLE

Raw data OE2: CSV JSON LATEX PICKLE

OE1 distribution for 0.1

Figure 25: OE1 for estimates compared to version 0.1. Shown are the mean OE1 and an empirical distribution of the sample, using kernel density estimation (KDE).

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OE2 distribution for 0.1

Figure 26: OE2 for estimates compared to version 0.1. Shown are the mean OE2 and an empirical distribution of the sample, using kernel density estimation (KDE).

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Mean OE1/OE2 Results for 1.0

Estimator OE1_MEAN OE1_STDEV OE2_MEAN OE2_STDEV
schreiber2018/cnn 0.0203 0.3155 0.0003 0.0031
schreiber2017/mirex2017 -0.0499 0.3281 0.0001 0.0019
schreiber2018/ismir2018 -0.0098 0.3318 0.0002 0.0029
schreiber2017/ismir2017 -0.0699 0.3541 0.0001 0.0019
schreiber2014/default -0.1335 0.3677 -0.0135 0.0687
percival2014/stem -0.1098 0.3720 0.0002 0.0027
schreiber2018/fcn -0.0398 0.3725 0.0002 0.0035
davies2009/mirex_qm_tempotracker 0.1541 0.4339 0.0241 0.0415
boeck2015/tempodetector2016_default -0.3359 0.5021 -0.0001 0.0054

Table 10: Mean OE1/OE2 for estimates compared to version 1.0 ordered by standard deviation.

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Raw data OE1: CSV JSON LATEX PICKLE

Raw data OE2: CSV JSON LATEX PICKLE

OE1 distribution for 1.0

Figure 27: OE1 for estimates compared to version 1.0. Shown are the mean OE1 and an empirical distribution of the sample, using kernel density estimation (KDE).

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OE2 distribution for 1.0

Figure 28: OE2 for estimates compared to version 1.0. Shown are the mean OE2 and an empirical distribution of the sample, using kernel density estimation (KDE).

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Significance of Differences

Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0049 0.0001 0.0001
percival2014/stem 0.0001 0.0000 1.0000 0.4618 0.2522 0.0580 0.0006 0.0710 0.0013
schreiber2014/default 0.0001 0.0000 0.4618 1.0000 0.0300 0.0040 0.0001 0.0319 0.0012
schreiber2017/ismir2017 0.0000 0.0000 0.2522 0.0300 1.0000 0.3197 0.0116 0.4061 0.0569
schreiber2017/mirex2017 0.0000 0.0000 0.0580 0.0040 0.3197 1.0000 0.0507 0.7957 0.1574
schreiber2018/cnn 0.0000 0.0049 0.0006 0.0001 0.0116 0.0507 1.0000 0.0330 0.4058
schreiber2018/fcn 0.0000 0.0001 0.0710 0.0319 0.4061 0.7957 0.0330 1.0000 0.4696
schreiber2018/ismir2018 0.0000 0.0001 0.0013 0.0012 0.0569 0.1574 0.4058 0.4696 1.0000

Table 11: Paired t-test p-values, using reference annotations 1.0 as groundtruth with OE1. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0049 0.0001 0.0001
percival2014/stem 0.0001 0.0000 1.0000 0.4618 0.2522 0.0580 0.0006 0.0710 0.0013
schreiber2014/default 0.0001 0.0000 0.4618 1.0000 0.0300 0.0040 0.0001 0.0319 0.0012
schreiber2017/ismir2017 0.0000 0.0000 0.2522 0.0300 1.0000 0.3197 0.0116 0.4061 0.0569
schreiber2017/mirex2017 0.0000 0.0000 0.0580 0.0040 0.3197 1.0000 0.0507 0.7957 0.1574
schreiber2018/cnn 0.0000 0.0049 0.0006 0.0001 0.0116 0.0507 1.0000 0.0330 0.4058
schreiber2018/fcn 0.0000 0.0001 0.0710 0.0319 0.4061 0.7957 0.0330 1.0000 0.4696
schreiber2018/ismir2018 0.0000 0.0001 0.0013 0.0012 0.0569 0.1574 0.4058 0.4696 1.0000

Table 12: Paired t-test p-values, using reference annotations 0.1 as groundtruth with OE1. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.5752 0.0536 0.7522 0.7522 0.4778 0.6639 0.6508
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
percival2014/stem 0.5752 0.0000 1.0000 0.0509 0.5002 0.5002 0.7342 0.8953 0.8052
schreiber2014/default 0.0536 0.0000 0.0509 1.0000 0.0524 0.0524 0.0481 0.0504 0.0506
schreiber2017/ismir2017 0.7522 0.0000 0.5002 0.0524 1.0000 0.4277 0.4172 0.7474 0.7364
schreiber2017/mirex2017 0.7522 0.0000 0.5002 0.0524 0.4277 1.0000 0.4172 0.7474 0.7364
schreiber2018/cnn 0.4778 0.0000 0.7342 0.0481 0.4172 0.4172 1.0000 0.5465 0.4596
schreiber2018/fcn 0.6639 0.0000 0.8953 0.0504 0.7474 0.7474 0.5465 1.0000 0.9230
schreiber2018/ismir2018 0.6508 0.0000 0.8052 0.0506 0.7364 0.7364 0.4596 0.9230 1.0000

Table 13: Paired t-test p-values, using reference annotations 1.0 as groundtruth with OE2. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.5752 0.0536 0.7522 0.7522 0.4778 0.6639 0.6508
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
percival2014/stem 0.5752 0.0000 1.0000 0.0509 0.5002 0.5002 0.7342 0.8953 0.8052
schreiber2014/default 0.0536 0.0000 0.0509 1.0000 0.0524 0.0524 0.0481 0.0504 0.0506
schreiber2017/ismir2017 0.7522 0.0000 0.5002 0.0524 1.0000 0.4277 0.4172 0.7474 0.7364
schreiber2017/mirex2017 0.7522 0.0000 0.5002 0.0524 0.4277 1.0000 0.4172 0.7474 0.7364
schreiber2018/cnn 0.4778 0.0000 0.7342 0.0481 0.4172 0.4172 1.0000 0.5465 0.4596
schreiber2018/fcn 0.6639 0.0000 0.8953 0.0504 0.7474 0.7474 0.5465 1.0000 0.9230
schreiber2018/ismir2018 0.6508 0.0000 0.8052 0.0506 0.7364 0.7364 0.4596 0.9230 1.0000

Table 14: Paired t-test p-values, using reference annotations 0.1 as groundtruth with OE2. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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OE1 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

OE1 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 29: Mean OE1 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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OE1 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 30: Mean OE1 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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OE2 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

OE2 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 31: Mean OE2 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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OE2 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 32: Mean OE2 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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OE1 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean OE1 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

OE1 on Tempo-Subsets for 0.1

Figure 33: Mean OE1 for estimates compared to version 0.1 for tempo intervals around T.

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OE1 on Tempo-Subsets for 1.0

Figure 34: Mean OE1 for estimates compared to version 1.0 for tempo intervals around T.

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OE2 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean OE2 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

OE2 on Tempo-Subsets for 0.1

Figure 35: Mean OE2 for estimates compared to version 0.1 for tempo intervals around T.

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OE2 on Tempo-Subsets for 1.0

Figure 36: Mean OE2 for estimates compared to version 1.0 for tempo intervals around T.

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Estimated OE1 for Tempo

When fitting a generalized additive model (GAM) to OE1-values and a ground truth, what OE1 can we expect with confidence?

Estimated OE1 for Tempo for 0.1

Predictions of GAMs trained on OE1 for estimates for reference 0.1.

Figure 37: OE1 predictions of a generalized additive model (GAM) fit to OE1 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated OE1 for Tempo for 1.0

Predictions of GAMs trained on OE1 for estimates for reference 1.0.

Figure 38: OE1 predictions of a generalized additive model (GAM) fit to OE1 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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Estimated OE2 for Tempo

When fitting a generalized additive model (GAM) to OE2-values and a ground truth, what OE2 can we expect with confidence?

Estimated OE2 for Tempo for 0.1

Predictions of GAMs trained on OE2 for estimates for reference 0.1.

Figure 39: OE2 predictions of a generalized additive model (GAM) fit to OE2 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated OE2 for Tempo for 1.0

Predictions of GAMs trained on OE2 for estimates for reference 1.0.

Figure 40: OE2 predictions of a generalized additive model (GAM) fit to OE2 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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OE1 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

OE1 for ‘tag_open’ Tags for 0.1

Figure 41: OE1 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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OE1 for ‘tag_open’ Tags for 1.0

Figure 42: OE1 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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OE2 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

OE2 for ‘tag_open’ Tags for 0.1

Figure 43: OE2 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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OE2 for ‘tag_open’ Tags for 1.0

Figure 44: OE2 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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AOE1 and AOE2

AOE1 is defined as absolute octave error between an estimate and a reference value: AOE1(E) = |log2(E/R)|.

AOE2 is the minimum of AOE1 allowing the octave errors 2, 3, 1/2, and 1/3: AOE2(E) = min(AOE1(E), AOE1(2E), AOE1(3E), AOE1(½E), AOE1(⅓E)).

Mean AOE1/AOE2 Results for 0.1

Estimator AOE1_MEAN AOE1_STDEV AOE2_MEAN AOE2_STDEV
schreiber2018/cnn 0.0942 0.2870 0.0042 0.0167
schreiber2017/mirex2017 0.1027 0.2971 0.0041 0.0149
schreiber2017/ismir2017 0.1227 0.3224 0.0041 0.0149
schreiber2018/ismir2018 0.1235 0.3250 0.0041 0.0167
schreiber2018/fcn 0.1336 0.3347 0.0044 0.0167
percival2014/stem 0.1447 0.3448 0.0055 0.0157
schreiber2014/default 0.1546 0.3430 0.0184 0.0700
davies2009/mirex_qm_tempotracker 0.2379 0.4082 0.0271 0.0438
boeck2015/tempodetector2016_default 0.3505 0.4828 0.0070 0.0158

Table 15: Mean AOE1/AOE2 for estimates compared to version 0.1 ordered by mean.

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Raw data AOE1: CSV JSON LATEX PICKLE

Raw data AOE2: CSV JSON LATEX PICKLE

AOE1 distribution for 0.1

Figure 45: AOE1 for estimates compared to version 0.1. Shown are the mean AOE1 and an empirical distribution of the sample, using kernel density estimation (KDE).

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AOE2 distribution for 0.1

Figure 46: AOE2 for estimates compared to version 0.1. Shown are the mean AOE2 and an empirical distribution of the sample, using kernel density estimation (KDE).

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Mean AOE1/AOE2 Results for 1.0

Estimator AOE1_MEAN AOE1_STDEV AOE2_MEAN AOE2_STDEV
schreiber2018/cnn 0.1016 0.2993 0.0018 0.0025
schreiber2017/mirex2017 0.1114 0.3126 0.0015 0.0011
schreiber2018/ismir2018 0.1116 0.3126 0.0017 0.0024
schreiber2017/ismir2017 0.1314 0.3362 0.0015 0.0011
schreiber2018/fcn 0.1419 0.3467 0.0020 0.0029
percival2014/stem 0.1521 0.3568 0.0022 0.0016
schreiber2014/default 0.1631 0.3555 0.0156 0.0683
davies2009/mirex_qm_tempotracker 0.2249 0.4018 0.0241 0.0415
boeck2015/tempodetector2016_default 0.3587 0.4861 0.0036 0.0040

Table 16: Mean AOE1/AOE2 for estimates compared to version 1.0 ordered by mean.

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Raw data AOE1: CSV JSON LATEX PICKLE

Raw data AOE2: CSV JSON LATEX PICKLE

AOE1 distribution for 1.0

Figure 47: AOE1 for estimates compared to version 1.0. Shown are the mean AOE1 and an empirical distribution of the sample, using kernel density estimation (KDE).

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AOE2 distribution for 1.0

Figure 48: AOE2 for estimates compared to version 1.0. Shown are the mean AOE2 and an empirical distribution of the sample, using kernel density estimation (KDE).

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Significance of Differences

Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0612 0.0005 0.0002 0.0001 0.0000 0.0000 0.0002 0.0000
davies2009/mirex_qm_tempotracker 0.0612 1.0000 0.1416 0.2366 0.0437 0.0099 0.0090 0.0997 0.0066
percival2014/stem 0.0005 0.1416 1.0000 0.7316 0.5534 0.1995 0.1933 0.7949 0.2019
schreiber2014/default 0.0002 0.2366 0.7316 1.0000 0.2832 0.0785 0.1389 0.6315 0.1858
schreiber2017/ismir2017 0.0001 0.0437 0.5534 0.2832 1.0000 0.3195 0.4106 0.7714 0.5333
schreiber2017/mirex2017 0.0000 0.0099 0.1995 0.0785 0.3195 1.0000 0.7872 0.4324 0.9944
schreiber2018/cnn 0.0000 0.0090 0.1933 0.1389 0.4106 0.7872 1.0000 0.1545 0.7832
schreiber2018/fcn 0.0002 0.0997 0.7949 0.6315 0.7714 0.4324 0.1545 1.0000 0.4639
schreiber2018/ismir2018 0.0000 0.0066 0.2019 0.1858 0.5333 0.9944 0.7832 0.4639 1.0000

Table 17: Paired t-test p-values, using reference annotations 1.0 as groundtruth with AOE1. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.1159 0.0006 0.0002 0.0001 0.0000 0.0000 0.0002 0.0002
davies2009/mirex_qm_tempotracker 0.1159 1.0000 0.0596 0.1092 0.0122 0.0019 0.0023 0.0380 0.0060
percival2014/stem 0.0006 0.0596 1.0000 0.7574 0.5238 0.1809 0.1913 0.7731 0.5049
schreiber2014/default 0.0002 0.1092 0.7574 1.0000 0.2798 0.0774 0.1434 0.6333 0.4237
schreiber2017/ismir2017 0.0001 0.0122 0.5238 0.2798 1.0000 0.3198 0.4277 0.7621 0.9785
schreiber2017/mirex2017 0.0000 0.0019 0.1809 0.0774 0.3198 1.0000 0.8136 0.4252 0.4593
schreiber2018/cnn 0.0000 0.0023 0.1913 0.1434 0.4277 0.8136 1.0000 0.1610 0.4170
schreiber2018/fcn 0.0002 0.0380 0.7731 0.6333 0.7621 0.4252 0.1610 1.0000 0.8066
schreiber2018/ismir2018 0.0002 0.0060 0.5049 0.4237 0.9785 0.4593 0.4170 0.8066 1.0000

Table 18: Paired t-test p-values, using reference annotations 0.1 as groundtruth with AOE1. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.0015 0.0848 0.0000 0.0000 0.0001 0.0007 0.0000
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.2957 0.0000 0.0000 0.0000 0.0000 0.0000
percival2014/stem 0.0015 0.0000 1.0000 0.0534 0.0001 0.0001 0.1158 0.5593 0.0199
schreiber2014/default 0.0848 0.2957 0.0534 1.0000 0.0430 0.0430 0.0483 0.0518 0.0462
schreiber2017/ismir2017 0.0000 0.0000 0.0001 0.0430 1.0000 0.2449 0.1780 0.0565 0.3624
schreiber2017/mirex2017 0.0000 0.0000 0.0001 0.0430 0.2449 1.0000 0.1780 0.0565 0.3624
schreiber2018/cnn 0.0001 0.0000 0.1158 0.0483 0.1780 0.1780 1.0000 0.1481 0.1829
schreiber2018/fcn 0.0007 0.0000 0.5593 0.0518 0.0565 0.0565 0.1481 1.0000 0.0456
schreiber2018/ismir2018 0.0000 0.0000 0.0199 0.0462 0.3624 0.3624 0.1829 0.0456 1.0000

Table 19: Paired t-test p-values, using reference annotations 1.0 as groundtruth with AOE2. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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Estimator boeck2015/tempodetector2016_default davies2009/mirex_qm_tempotracker percival2014/stem schreiber2014/default schreiber2017/ismir2017 schreiber2017/mirex2017 schreiber2018/cnn schreiber2018/fcn schreiber2018/ismir2018
boeck2015/tempodetector2016_default 1.0000 0.0000 0.0000 0.1049 0.0000 0.0000 0.0000 0.0000 0.0000
davies2009/mirex_qm_tempotracker 0.0000 1.0000 0.0000 0.2862 0.0000 0.0000 0.0000 0.0000 0.0000
percival2014/stem 0.0000 0.0000 1.0000 0.0640 0.0000 0.0000 0.0000 0.0000 0.0000
schreiber2014/default 0.1049 0.2862 0.0640 1.0000 0.0417 0.0417 0.0429 0.0460 0.0407
schreiber2017/ismir2017 0.0000 0.0000 0.0000 0.0417 1.0000 0.4277 0.7770 0.3384 0.7668
schreiber2017/mirex2017 0.0000 0.0000 0.0000 0.0417 0.4277 1.0000 0.7770 0.3384 0.7669
schreiber2018/cnn 0.0000 0.0000 0.0000 0.0429 0.7770 0.7770 1.0000 0.3747 0.4596
schreiber2018/fcn 0.0000 0.0000 0.0000 0.0460 0.3384 0.3384 0.3747 1.0000 0.0474
schreiber2018/ismir2018 0.0000 0.0000 0.0000 0.0407 0.7668 0.7669 0.4596 0.0474 1.0000

Table 20: Paired t-test p-values, using reference annotations 0.1 as groundtruth with AOE2. H0: the true mean difference between paired samples is zero. If p<=ɑ, reject H0, i.e. we have a significant difference between estimates from the two algorithms. In the table, p-values<0.05 are set in bold.

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AOE1 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

AOE1 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 49: Mean AOE1 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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AOE1 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 50: Mean AOE1 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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AOE2 on cvar-Subsets

How well does an estimator perform, when only taking tracks into account that have a cvar-value of less than τ, i.e., have a more or less stable beat?

AOE2 on cvar-Subsets for 0.1 based on cvar-Values from 1.0

Figure 51: Mean AOE2 compared to version 0.1 for tracks with cvar < τ based on beat annotations from 0.1.

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AOE2 on cvar-Subsets for 1.0 based on cvar-Values from 1.0

Figure 52: Mean AOE2 compared to version 1.0 for tracks with cvar < τ based on beat annotations from 1.0.

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AOE1 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean AOE1 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

AOE1 on Tempo-Subsets for 0.1

Figure 53: Mean AOE1 for estimates compared to version 0.1 for tempo intervals around T.

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AOE1 on Tempo-Subsets for 1.0

Figure 54: Mean AOE1 for estimates compared to version 1.0 for tempo intervals around T.

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AOE2 on Tempo-Subsets

How well does an estimator perform, when only taking a subset of the reference annotations into account? The graphs show mean AOE2 for reference subsets with tempi in [T-10,T+10] BPM. Note that the graphs do not show confidence intervals and that some values may be based on very few estimates.

AOE2 on Tempo-Subsets for 0.1

Figure 55: Mean AOE2 for estimates compared to version 0.1 for tempo intervals around T.

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AOE2 on Tempo-Subsets for 1.0

Figure 56: Mean AOE2 for estimates compared to version 1.0 for tempo intervals around T.

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Estimated AOE1 for Tempo

When fitting a generalized additive model (GAM) to AOE1-values and a ground truth, what AOE1 can we expect with confidence?

Estimated AOE1 for Tempo for 0.1

Predictions of GAMs trained on AOE1 for estimates for reference 0.1.

Figure 57: AOE1 predictions of a generalized additive model (GAM) fit to AOE1 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated AOE1 for Tempo for 1.0

Predictions of GAMs trained on AOE1 for estimates for reference 1.0.

Figure 58: AOE1 predictions of a generalized additive model (GAM) fit to AOE1 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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Estimated AOE2 for Tempo

When fitting a generalized additive model (GAM) to AOE2-values and a ground truth, what AOE2 can we expect with confidence?

Estimated AOE2 for Tempo for 0.1

Predictions of GAMs trained on AOE2 for estimates for reference 0.1.

Figure 59: AOE2 predictions of a generalized additive model (GAM) fit to AOE2 results for 0.1. The 95% confidence interval around the prediction is shaded in gray.

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Estimated AOE2 for Tempo for 1.0

Predictions of GAMs trained on AOE2 for estimates for reference 1.0.

Figure 60: AOE2 predictions of a generalized additive model (GAM) fit to AOE2 results for 1.0. The 95% confidence interval around the prediction is shaded in gray.

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AOE1 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

AOE1 for ‘tag_open’ Tags for 0.1

Figure 61: AOE1 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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AOE1 for ‘tag_open’ Tags for 1.0

Figure 62: AOE1 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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AOE2 for ‘tag_open’ Tags

How well does an estimator perform, when only taking tracks into account that are tagged with some kind of label? Note that some values may be based on very few estimates.

AOE2 for ‘tag_open’ Tags for 0.1

Figure 63: AOE2 of estimates compared to version 0.1 depending on tag from namespace ‘tag_open’.

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AOE2 for ‘tag_open’ Tags for 1.0

Figure 64: AOE2 of estimates compared to version 1.0 depending on tag from namespace ‘tag_open’.

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Generated by tempo_eval 0.1.1 on 2022-06-29 18:54. Size L.