2016:Discovery of Repeated Themes & Sections Results

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Contents

Introduction

The task: algorithms take a piece of music as input, and output a list of patterns repeated within that piece. A pattern is defined as a set of ontime-pitch pairs that occurs at least twice (i.e., is repeated at least once) in a piece of music. The second, third, etc. occurrences of the pattern will likely be shifted in time and/or transposed, relative to the first occurrence. Ideally an algorithm will be able to discover all exact and inexact occurrences of a pattern within a piece, so in evaluating this task we are interested in both:

  • (1) to what extent an algorithm can discover one occurrence, up to time shift and transposition, and;
  • (2) to what extent it can find all occurrences.

The metrics establishment recall, establishment precision and establishment F1 address (1), and the metrics occurrence recall, occurrence precision, and occurrence F1 address (2).

Contribution

Existing approaches to music structure analysis in MIR tend to focus on segmentation (e.g., Weiss & Bello, 2010). The contribution of this task is to afford access to the note content itself (please see the example in Fig. 1A), requiring algorithms to do more than label time windows (e.g., the segmentations in Figs. 1B-D). For instance, a discovery algorithm applied to the piece in Fig. 1A should return a pattern corresponding to the note content of P1 and P2, as well as a pattern corresponding to the note content of Q1. This is because Q1 occurs again independently of the accompaniment in bars 19-22 (not shown here). The ground truth also contains nested patterns, such as P1 in Fig. 1A being a subset of the sectional repetition S1, reflecting the often-hierarchical nature of musical repetition. While we recognise the appealing simplicity of linear segmentation, in the Discovery of Repeated Themes & Sections task we are demanding analysis at a greater level of detail, and have built a ground truth that contains overlapping and nested patterns (Collins et al., 2014).


MozartK282Mvt2.png

Figure 1. Pattern discovery v segmentation. (A) Bars 1-12 of Mozart’s Piano Sonata in E-flat major K282 mvt.2, showing some ground-truth themes and repeated sections; (B-D) Three linear segmentations. Numbers below the staff in Fig. 1A and below the segmentation in Fig. 1D indicate crotchet beats, from zero for bar 1 beat 1.


For a more detailed introduction to the task, please see 2015:Discovery_of_Repeated_Themes_&_Sections.

Ground Truth and Algorithms

The ground truth, called the Johannes Kepler University Patterns Test Database (JKUPTD-Aug2013), is based on motifs and themes in Barlow and Morgenstern (1953), Schoenberg (1967), and Bruhn (1993). Repeated sections are based on those marked by the composer. These annotations are supplemented with some of our own where necessary. A Development Database (JKUPDD-Aug2013) enabled participants to try out their algorithms. For each piece in the Development and Test Databases, symbolic and synthesised audio versions are crossed with monophonic and polyphonic versions, giving four versions of the task in total: symPoly, symMono, audPoly, and audMono. There were no submissions to the audPoly or audMono categories this year, so two versions of the task ran. Submitted algorithms are shown in Table 1.


Sub code Submission name Abstract Contributors
Task Version symPoly
DM1 SIATECCompress-TLF1 PDF David Meredith
DM2 SIATECCompress-TLP PDF David Meredith
DM3 SIATECCompress-TLR PDF David Meredith
Task Version symMono
DM1 SIATECCompress-TLF1 PDF David Meredith
DM2 SIATECCompress-TLP PDF David Meredith
DM3 SIATECCompress-TLR PDF David Meredith
IR1 mypattern PDF Iris YuPing Ren
PLM1 SYMCHM PDF Matevž Pesek, Urša Medvešek, Aleš Leonardis, Matija Marolt
VM1'14 VM1 PDF Gissel Velarde, David Meredith
VM2'14 VM2 PDF Gissel Velarde, David Meredith

Table 1. Algorithms submitted to DRTS.

Results

(For mathematical definitions of the various metrics, please see 2015:Discovery_of_Repeated_Themes_&_Sections#Evaluation_Procedure.)

symMono

We welcomed a new participant (Ren, 2016) to the symMono version of the task. All other researchers participated in previous years, but some (Meredith, 2016; Pesek, Leonardis, & Marolt, 2016) submitted new versions of algorithms.

To recap some information from previous years, the metrics that can be calculated on a per-pattern basis (establishment recall and occurrence recall) present opportunities to test for significant differences in performance between algorithms. For the remaining metrics, which can only be calculated on a per-piece basis, we do not test for significant differences because the sample size is too small. Still, however, trends in results are evident in the figures and tables below. For instance, in the symMono task version, VM1's (Velarde & Meredith, 2016) establishment F1 score outperforms all other algorithms apart from for one piece (Fig. 18). It is also the stand-out performer on establishment recall (Fig. 14). IR1 does particularly well for some of the smaller/shorter patterns in piece 5 (Fig. 14), which other algorithms seem to miss.

Application of Friedman's test to establishment recall per pattern results revealed a significant main effect of algorithm (\chi^2(3) = 54.5062,\ p < .001). Bonferroni-corrected, pairwise tests suggested the ordering VM1 > DM1 ~ IR1 > PLM1, where > denotes a significant difference and ~ denotes no significant difference. Application of Friedman's test to occurrence recall per pattern results revealed a significant main effect of algorithm (\chi^2(3) = 54.5062,\ p < .001). Bonferroni-corrected, pairwise tests suggested the ordering VM1 > DM1 ~ PLM1 ~ IR1. One of these tests was only borderline-significant (that between VM1 and PLM1), but this is probably due to averaging results for PLM1 on piece 5 (see below or Fig. 15).

With regards runtimes (Fig. 25), it should be noted that those for PLM1 and IR1 are somewhat harsh, because the submissions had to be run on slower machines than the Linux cluster on which the other submissions were ran. This is a function of programming language/operating system used by the researchers. After running for one week on piece 5 (the longest piece), PLM1 did not produce output, so was assigned mean values over the remaining pieces (Figs. 14 and 15). It was assigned the maximum runtime over the remaining pieces (Fig. 25). The task captain accepts some responsibility for such issues: he should have made the longest piece in the development database far longer than any piece in the test database!

PLM1, DM2, VM1, and VM2 output far fewer patterns than other algorithms (see the n_Q column in Table 4). One potential application of pattern discovery algorithms is for playback accompanied by a visualization of repetitive structure. The relatively few patterns output by these algorithms make them more feasible candidates for this application.

symPoly

Meredith (2016) submitted three algorithms to the symPoly task version, selected according to performance on three-layer F1, precision, and recall metrics, respectively. DM3 outperforms DM2 on establishment recall (\chi^2(1) = 11.9189,\ p < .05, Bonferroni corrected), which is perhaps to be expected because DM3 was submitted by Meredith (2016) on the basis of a recall metric, whereas DM2 was submitted on the basis of a precision metric. Similarly, DM1 outperforms DM2 on establishment recall (\chi^2(1) = 23.5161,\ p < .005, Bonferroni corrected). Again, this is not particularly surprising because DM1 was submitted on the basis of an F1 performance metric (which combines recall and precision), whereas DM2 was submitted on the basis of a precision metric. DM3 is generally higher than DM2 on three-layer recall, and DM2 is mostly higher than DM3 on three-layer precision. Both of these results make sense, given the basis on upon which these algorithms were submitted.

Discussion

To be added post-ISMIR!

I certainly have to step down as Task Captain next year, because I have too many other commitments.

It has already been discussed that next year we may switch to new training and test databases that focus not directly on the discovery task itself, but on an application of pattern discovery that can be used as a proxy to evaluate the extent to which algorithms have retrieved relevant repetitive material. For example, a prediction task. This may also bring interest from deep learners and/or cognitive scientists, looking to build on previous participants' work, which would be great. If you are interested in helping out with the preparation of a new version of the task, you are welcome to get in touch.

Tom Collins, New York, 2016

Results in Detail

symPoly

01symPolyEstRecPerPatt2016.png

Figure 2. Establishment recall on a per-pattern basis. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?


04symPolyOccRecPerPatt2016.png

Figure 3. Occurrence recall on a per-pattern basis. Occurrence recall answers the following question. On average, how similar is the most similar set of algorithm-output pattern occurrences to a discovered ground-truth occurrence set?


01symPolyEstRec2016.png

Figure 4. Establishment recall averaged over each piece/movement. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?


02symPolyEstPrec2016.png

Figure 5. Establishment precision averaged over each piece/movement. Establishment precision answers the following question. On average, how similar is the most similar ground-truth pattern prototype to an algorithm-output pattern?


03symPolyEstF12016.png

Figure 6. Establishment F1 averaged over each piece/movement. Establishment F1 is an average of establishment precision and establishment recall.


04symPolyOccRecP752016.png

Figure 7. Occurrence recall (c = .75) averaged over each piece/movement. Occurrence recall answers the following question. On average, how similar is the most similar set of algorithm-output pattern occurrences to a discovered ground-truth occurrence set?


05symPolyOccPrecP752016.png

Figure 8. Occurrence precision (c = .75) averaged over each piece/movement. Occurrence precision answers the following question. On average, how similar is the most similar discovered ground-truth occurrence set to a set of algorithm-output pattern occurrences?


06symPolyOccF1P752016.png

Figure 9. Occurrence F1 (c = .75) averaged over each piece/movement. Occurrence F1 is an average of occurrence precision and occurrence recall.


07symPolyR32016.png

Figure 10. Three-layer recall averaged over each piece/movement. Rather than using |P \cap Q|/\max\{|P|, |Q|\} as a similarity measure (which is the default for establishment recall), three-layer recall uses 2|P \cap Q|/(|P| + |Q|), which is a kind of F1 measure.


08symPolyP32016.png

Figure 11. Three-layer precision averaged over each piece/movement. Rather than using |P \cap Q|/\max\{|P|, |Q|\} as a similarity measure (which is the default for establishment precision), three-layer precision uses 2|P \cap Q|/(|P| + |Q|), which is a kind of F1 measure.


09symPolyTLF2016.png

Figure 12. Three-layer F1 (TLF) averaged over each piece/movement. TLF is an average of three-layer precision and three-layer recall.


10symPolyRuntime2016.png

Figure 13. Log runtime of the algorithm for each piece/movement.

symMono

(Submission PLM1 did not complete on piece 5. The task captain took the decision to assign the mean of the evaluation metrics for PLM1 calculated across the remaining pieces. Apart from runtime, in which case the maximum across the remaining pieces was assigned.)

11symMonoEstRecPerPatt2016.png

Figure 14. Establishment recall on a per-pattern basis. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?


14symMonoOccRecPerPatt2016.png

Figure 15. Occurrence recall on a per-pattern basis. Occurrence recall answers the following question. On average, how similar is the most similar set of algorithm-output pattern occurrences to a discovered ground-truth occurrence set?


11symMonoEstRec2016.png

Figure 16. Establishment recall averaged over each piece/movement. Establishment recall answers the following question. On average, how similar is the most similar algorithm-output pattern to a ground-truth pattern prototype?


12symMonoEstPrec2016.png

Figure 17. Establishment precision averaged over each piece/movement. Establishment precision answers the following question. On average, how similar is the most similar ground-truth pattern prototype to an algorithm-output pattern?


13symMonoEstF12016.png

Figure 18. Establishment F1 averaged over each piece/movement. Establishment F1 is an average of establishment precision and establishment recall.


14symMonoOccRecP752016.png

Figure 19. Occurrence recall (c = .75) averaged over each piece/movement. Occurrence recall answers the following question. On average, how similar is the most similar set of algorithm-output pattern occurrences to a discovered ground-truth occurrence set?


15symMonoOccPrecP752016.png

Figure 20. Occurrence precision (c = .75) averaged over each piece/movement. Occurrence precision answers the following question. On average, how similar is the most similar discovered ground-truth occurrence set to a set of algorithm-output pattern occurrences?


16symMonoOccF1P752016.png

Figure 21. Occurrence F1 (c = .75) averaged over each piece/movement. Occurrence F1 is an average of occurrence precision and occurrence recall.


17symMonoR32016.png

Figure 22. Three-layer recall averaged over each piece/movement. Rather than using |P \cap Q|/\max\{|P|, |Q|\} as a similarity measure (which is the default for establishment recall), three-layer recall uses 2|P \cap Q|/(|P| + |Q|), which is a kind of F1 measure.


18symMonoP32016.png

Figure 23. Three-layer precision averaged over each piece/movement. Rather than using |P \cap Q|/\max\{|P|, |Q|\} as a similarity measure (which is the default for establishment precision), three-layer precision uses 2|P \cap Q|/(|P| + |Q|), which is a kind of F1 measure.


19symMonoTLF2016.png

Figure 24. Three-layer F1 (TLF) averaged over each piece/movement. TLF is an average of three-layer precision and three-layer recall.


20symMonoRuntime2016.png

Figure 25. Log runtime of the algorithm for each piece/movement.

Tabular Versions of Plots

symPoly

AlgIdx AlgStub Piece n_P n_Q P_est R_est F1_est P_occ(c=.75) R_occ(c=.75) F_1occ(c=.75) P_3 R_3 TLF_1 runtime FRT FFTP_est FFP P_occ(c=.5) R_occ(c=.5) F_1occ(c=.5) P R F_1
1 DM1 piece1 5 30.000 0.328 0.431 0.372 0.000 0.000 0.000 0.215 0.326 0.259 74.000 0.000 0.366 0.330 0.284 0.430 0.342 0.000 0.000 0.000
1 DM1 piece2 5 63.000 0.565 0.661 0.609 0.508 0.936 0.658 0.438 0.468 0.452 727.000 0.000 0.285 0.357 0.485 0.944 0.641 0.000 0.000 0.000
1 DM1 piece3 10.000 28.000 0.703 0.685 0.694 0.520 0.784 0.625 0.620 0.536 0.575 35.000 0.000 0.447 0.688 0.496 0.749 0.597 0.000 0.000 0.000
1 DM1 piece4 5 19.000 0.410 0.495 0.448 0.000 0.000 0.000 0.327 0.340 0.333 2.000 0.000 0.416 0.271 0.255 0.564 0.351 0.000 0.000 0.000
1 DM1 piece5 13.000 56.000 0.766 0.612 0.680 0.543 0.941 0.688 0.634 0.467 0.538 1894.000 0.000 0.391 0.686 0.513 0.890 0.651 0.000 0.000 0.000
2 DM2 piece1 5 10.000 0.353 0.291 0.319 0.000 0.000 0.000 0.242 0.174 0.202 77.000 0.000 0.291 0.268 0.424 0.156 0.228 0.000 0.000 0.000
2 DM2 piece2 5 10.000 0.320 0.306 0.313 0.000 0.000 0.000 0.295 0.253 0.273 710.000 0.000 0.306 0.449 0.388 0.692 0.497 0.000 0.000 0.000
2 DM2 piece3 10.000 10.000 0.777 0.497 0.606 0.710 0.802 0.753 0.737 0.450 0.559 33.000 0.000 0.435 0.711 0.651 0.775 0.708 0.000 0.000 0.000
2 DM2 piece4 5 10.000 0.519 0.476 0.496 0.565 0.952 0.709 0.450 0.293 0.355 1.000 0.000 0.280 0.626 0.441 0.839 0.578 0.000 0.000 0.000
2 DM2 piece5 13.000 10.000 0.857 0.397 0.543 0.614 0.913 0.734 0.717 0.358 0.478 1734.000 0.000 0.391 0.686 0.597 0.857 0.704 0.000 0.000 0.000
3 DM3 piece1 5 41.000 0.357 0.528 0.426 0.406 0.439 0.422 0.257 0.422 0.319 509.000 0.000 0.498 0.439 0.369 0.516 0.430 0.000 0.000 0.000
3 DM3 piece2 5 71.000 0.621 0.691 0.654 0.605 0.945 0.738 0.559 0.599 0.578 2109.000 0.000 0.290 0.305 0.533 0.908 0.672 0.000 0.000 0.000
3 DM3 piece3 10.000 25.000 0.594 0.589 0.592 0.528 0.777 0.629 0.585 0.522 0.551 89.000 0.000 0.302 0.687 0.481 0.728 0.580 0.000 0.000 0.000
3 DM3 piece4 5 24.000 0.465 0.488 0.476 0.355 0.765 0.485 0.352 0.337 0.344 2.000 0.000 0.415 0.583 0.279 0.617 0.384 0.000 0.000 0.000
3 DM3 piece5 13.000 75.000 0.697 0.470 0.561 0.644 0.948 0.767 0.657 0.443 0.529 4765.000 0.000 0.205 0.739 0.598 0.881 0.713 0.000 0.000 0.000

download these results as csv

Table 2. Tabular version of Figures 4-13.


AlgIdx AlgStub Piece n_P R_est R_occ(c=.75) R_occ(c=.5)
1 DM1 piece1 5
0.882 0.900 0.500 0.450 0.867
0.866 0.613 0.000 0.000 0.778
0.866 0.613 0.000 0.000 0.778
1 DM1 piece2 5
0.444 0.875 0.500 0.908 0.927
0.00000 0.802 0.000 0.908 0.923
0.00000 0.802 0.000 0.908 0.923
1 DM1 piece3 10.000
0.455 0.605 0.767 0.364 0.500 0.250 0.045 0.562 0.417 0.333
0.00000 0.000 0.678 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.678 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 DM1 piece4 8
1.00000 0.857 0.385 0.444 0.867 0.350 0.250 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
1 DM1 piece5 13.000
0.560 0.120 0.967 0.984 0.857 0.172 0.130 0.333 0.122 0.675 0.636 0.938 0.752
0.00000 0.000 0.967 0.949 0.711 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.752
0.00000 0.000 0.967 0.949 0.711 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.752
2 DM2 piece1 5
1.00000 1.000 0.125 0.000 0.682
0.866 0.613 0.000 0.000 0.000
0.866 0.613 0.000 0.000 0.000
2 DM2 piece2 5
0.056 0.348 0.021 0.876 0.419
0.00000 0.000 0.000 0.875 0.000
0.00000 0.000 0.000 0.875 0.000
2 DM2 piece3 10.000
0.349 0.612 0.619 0.163 0.500 0.250 0.045 0.562 0.146 0.083
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 DM2 piece4 8
0.667 0.538 0.625 0.571 0.469 0.643 0.450 0.591
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 DM2 piece5 13.000
0.094 0.092 0.350 0.273 0.254 0.122 0.051 0.069 0.084 0.462 0.243 0.938 0.515
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.000
3 DM3 piece1 5
0.882 0.857 0.333 0.273 0.867
0.866 0.613 0.000 0.000 0.778
0.866 0.613 0.000 0.000 0.778
3 DM3 piece2 5
0.800 0.438 0.333 0.672 0.920
0.739 0.000 0.000 0.000 0.913
0.739 0.000 0.000 0.000 0.913
3 DM3 piece3 10.000
0.600 0.674 0.722 1.000 0.579 0.355 0.065 0.795 1.000 0.667
0.00000 0.000 0.000 0.190 0.000 0.000 0.000 0.751 0.667 0.000
0.00000 0.000 0.000 0.190 0.000 0.000 0.000 0.751 0.667 0.000
3 DM3 piece4 8
1.00000 0.857 0.385 0.364 0.875 0.619 0.600 0.857
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
3 DM3 piece5 13.000
0.591 0.500 0.846 0.980 0.281 0.145 0.067 0.075 0.083 0.835 0.286 0.936 0.742
0.00000 0.000 0.846 0.947 0.000 0.000 0.000 0.000 0.000 0.504 0.000 0.924 0.000
0.00000 0.000 0.846 0.947 0.000 0.000 0.000 0.000 0.000 0.504 0.000 0.924 0.000
4 VM1 piece1 5
0.944 1.000 1.000 0.364 0.938
0.775 0.771 0.784 0.000 0.782
0.775 0.771 0.784 0.000 0.782
4 VM1 piece2 5
0.800 0.812 0.600 0.936 0.934
0.614 0.734 0.000 0.936 0.934
0.614 0.734 0.000 0.936 0.934
4 VM1 piece3 10.000
0.647 0.752 0.846 1.000 0.579 0.314 0.500 0.742 1.000 1.000
0.00000 0.752 0.724 0.831 0.000 0.000 0.000 0.000 0.905 1.000
0.00000 0.752 0.724 0.831 0.000 0.000 0.000 0.000 0.905 1.000
4 VM1 piece4 8
1.00000 0.857 0.600 1.000 1.000 1.000 0.900 1.000
0.860 0.429 0.000 1.000 1.000 0.912 0.750 1.000
0.860 0.429 0.000 1.000 1.000 0.912 0.750 1.000
4 VM1 piece5 13.000
0.857 0.714 0.989 0.993 0.968 0.400 0.125 0.529 0.500 0.771 0.800 0.668 0.573
0.857 0.000 0.989 0.993 0.968 0.000 0.000 0.000 0.000 0.366 0.746 0.000 0.000
0.857 0.000 0.989 0.993 0.968 0.000 0.000 0.000 0.000 0.366 0.746 0.000 0.000
5 VM2 piece1 5
0.727 0.719 0.625 0.111 0.667
0.00000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000
5 VM2 piece2 5
0.538 0.812 0.600 0.920 0.934
0.00000 0.734 0.000 0.920 0.934
0.00000 0.734 0.000 0.920 0.934
5 VM2 piece3 10.000
0.786 0.744 0.962 0.278 0.395 0.360 0.250 0.677 0.571 0.190
0.384 0.000 0.498 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.384 0.000 0.498 0.000 0.000 0.000 0.000 0.000 0.000 0.000
5 VM2 piece4 8
0.667 0.571 0.600 1.000 1.000 0.714 0.800 0.769
0.00000 0.000 0.000 0.911 1.000 0.000 0.733 0.769
0.00000 0.000 0.000 0.911 1.000 0.000 0.733 0.769
5 VM2 piece5 13.000
0.609 0.087 0.974 0.982 0.633 0.600 0.333 0.111 0.084 0.800 0.667 0.652 0.488
0.00000 0.000 0.974 0.982 0.000 0.000 0.000 0.000 0.000 0.388 0.000 0.000 0.000
0.00000 0.000 0.974 0.982 0.000 0.000 0.000 0.000 0.000 0.388 0.000 0.000 0.000
6 IR1 piece1 5
0.765 0.684 0.714 0.750 0.600
0.074 0.000 0.000 0.375 0.000
0.074 0.000 0.000 0.375 0.000
6 IR1 piece2 5
0.727 0.812 0.600 0.024 0.154
0.00000 0.109 0.000 0.000 0.000
0.00000 0.109 0.000 0.000 0.000
6 IR1 piece3 10.000
0.647 0.062 0.742 0.833 0.237 0.474 0.250 0.839 0.857 0.750
0.00000 0.000 0.000 0.144 0.000 0.000 0.000 0.468 0.238 0.188
0.00000 0.000 0.000 0.144 0.000 0.000 0.000 0.468 0.238 0.188
6 IR1 piece4 8
0.600 0.429 0.000 0.800 0.133 0.143 0.778 0.692
0.00000 0.000 0.000 0.177 0.000 0.000 0.389 0.000
0.00000 0.000 0.000 0.177 0.000 0.000 0.389 0.000
6 IR1 piece5 13.000
0.500 0.625 0.015 0.081 0.333 0.364 0.700 0.889 0.800 0.359 0.652 0.134 0.181
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.136 0.198 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.136 0.198 0.000 0.000 0.000 0.000
7 PLM1 piece1 5
0.941 0.842 0.077 0.000 0.000
0.309 0.265 0.000 0.000 0.000
0.309 0.265 0.000 0.000 0.000
7 PLM1 piece2 5
0.333 0.552 0.125 0.116 0.053
0.00000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000
7 PLM1 piece3 10.000
0.312 0.357 0.553 0.146 0.474 0.344 0.062 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
7 PLM1 piece4 8
0.250 0.500 0.000 0.286 0.000 1.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 1.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 1.000 0.000 0.000
7 PLM1 piece5 13.000
0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263
0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054
0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054

download these results as csv

Table 3. Tabular version of Figures 2 and 3.


symMono

AlgIdx AlgStub Piece n_P n_Q P_est R_est F1_est P_occ(c=.75) R_occ(c=.75) F_1occ(c=.75) P_3 R_3 TLF_1 runtime FRT FFTP_est FFP P_occ(c=.5) R_occ(c=.5) F_1occ(c=.5) P R F_1
1 DM1 piece1 5 44.000 0.410 0.720 0.523 0.470 0.689 0.559 0.237 0.537 0.328 152.000 0.000 0.518 0.606 0.388 0.695 0.498 0.000 0.000 0.000
1 DM1 piece2 5 47.000 0.535 0.731 0.618 0.518 0.904 0.659 0.453 0.616 0.522 90.000 0.000 0.208 0.344 0.434 0.883 0.582 0.000 0.000 0.000
1 DM1 piece3 10.000 13.000 0.484 0.430 0.455 0.678 0.678 0.678 0.481 0.386 0.429 2.000 0.000 0.359 0.542 0.402 0.605 0.483 0.000 0.000 0.000
1 DM1 piece4 8 24.000 0.331 0.625 0.432 0.524 0.771 0.624 0.274 0.425 0.333 1.000 0.000 0.519 0.497 0.445 0.802 0.572 0.042 0.125 0.062
1 DM1 piece5 13.000 72.000 0.740 0.558 0.636 0.538 0.930 0.682 0.640 0.445 0.525 688.000 0.000 0.263 0.650 0.517 0.869 0.648 0.000 0.000 0.000
2 DM2 piece1 5 10.000 0.709 0.561 0.627 0.687 0.697 0.692 0.448 0.435 0.441 139.000 0.000 0.436 0.534 0.541 0.699 0.610 0.100 0.200 0.133
2 DM2 piece2 5 10.000 0.395 0.344 0.368 0.524 0.875 0.656 0.375 0.277 0.319 64.000 0.000 0.208 0.344 0.350 0.875 0.500 0.000 0.000 0.000
2 DM2 piece3 10.000 10.000 0.520 0.333 0.406 0.000 0.000 0.000 0.490 0.342 0.403 2.000 0.000 0.333 0.534 0.370 0.536 0.438 0.000 0.000 0.000
2 DM2 piece4 8 10.000 0.290 0.569 0.384 0.000 0.000 0.000 0.205 0.237 0.220 1.000 0.000 0.197 0.225 0.166 0.405 0.236 0.000 0.000 0.000
2 DM2 piece5 13.000 10.000 0.821 0.273 0.409 0.564 0.926 0.701 0.683 0.234 0.349 575.000 0.000 0.263 0.650 0.540 0.865 0.665 0.000 0.000 0.000
3 DM3 piece1 5 44.000 0.364 0.642 0.465 0.518 0.717 0.602 0.224 0.520 0.313 156.000 0.000 0.518 0.606 0.404 0.736 0.521 0.000 0.000 0.000
3 DM3 piece2 5 47.000 0.544 0.633 0.585 0.673 0.886 0.765 0.508 0.571 0.538 234.000 0.000 0.572 0.578 0.545 0.824 0.656 0.021 0.200 0.038
3 DM3 piece3 10.000 14.000 0.633 0.646 0.639 0.700 0.536 0.607 0.534 0.523 0.528 4.000 0.000 0.294 0.588 0.502 0.565 0.531 0.071 0.100 0.083
3 DM3 piece4 8 26.000 0.376 0.695 0.488 0.398 0.804 0.532 0.260 0.404 0.316 2.000 0.000 0.671 0.505 0.287 0.709 0.409 0.038 0.125 0.059
3 DM3 piece5 13.000 60.000 0.618 0.490 0.547 0.570 0.866 0.688 0.589 0.414 0.486 848.000 0.000 0.311 0.705 0.528 0.806 0.638 0.000 0.000 0.000
4 VM1 piece1 5 7 0.557 0.849 0.673 0.360 0.777 0.492 0.344 0.362 0.353 61.569 0.000 0.849 0.482 0.372 0.777 0.503 0.000 0.000 0.000
4 VM1 piece2 5 7 0.412 0.817 0.548 0.486 0.805 0.606 0.320 0.430 0.367 393.263 0.000 0.472 0.074 0.399 0.764 0.524 0.286 0.400 0.333
4 VM1 piece3 10.000 7 0.773 0.738 0.755 0.442 0.823 0.575 0.627 0.521 0.569 12.921 0.000 0.628 0.557 0.533 0.703 0.606 0.000 0.000 0.000
4 VM1 piece4 8 7 0.932 0.920 0.926 0.644 0.869 0.740 0.749 0.620 0.679 66.093 0.000 0.812 0.735 0.526 0.855 0.651 0.286 0.250 0.267
4 VM1 piece5 13.000 7 0.819 0.684 0.745 0.512 0.755 0.610 0.675 0.400 0.503 51.341 0.000 0.566 0.549 0.414 0.656 0.507 0.286 0.154 0.200
5 VM2 piece1 5 5 0.540 0.570 0.555 0.000 0.000 0.000 0.286 0.207 0.240 18.250 0.000 0.570 0.286 0.291 0.490 0.365 0.000 0.000 0.000
5 VM2 piece2 5 7 0.446 0.761 0.562 0.649 0.863 0.741 0.357 0.488 0.413 32.966 0.000 0.419 0.128 0.427 0.630 0.509 0.143 0.200 0.167
5 VM2 piece3 10.000 7 0.690 0.521 0.594 0.865 0.441 0.584 0.609 0.471 0.531 3.580 0.000 0.393 0.541 0.662 0.561 0.607 0.000 0.000 0.000
5 VM2 piece4 8 6 0.842 0.765 0.802 0.579 0.837 0.684 0.732 0.504 0.597 5.863 0.000 0.721 0.711 0.410 0.732 0.525 0.167 0.125 0.143
5 VM2 piece5 13.000 7 0.739 0.540 0.624 0.910 0.781 0.841 0.677 0.434 0.529 37.876 0.000 0.420 0.554 0.517 0.636 0.570 0.000 0.000 0.000
6 IR1 piece1 5 100.000 0.557 0.703 0.622 0.123 0.188 0.149 0.177 0.256 0.209 388.120 0.000 0.499 0.208 0.175 0.191 0.183 0.000 0.000 0.000
6 IR1 piece2 5 100.000 0.685 0.464 0.553 0.074 0.109 0.088 0.093 0.105 0.099 7384.700 0.000 0.459 0.084 0.070 0.096 0.081 0.000 0.000 0.000
6 IR1 piece3 10.000 100.000 0.820 0.569 0.672 0.212 0.301 0.249 0.274 0.201 0.232 52.520 0.000 0.449 0.279 0.228 0.223 0.225 0.000 0.000 0.000
6 IR1 piece4 8 80.000 0.544 0.447 0.491 0.371 0.283 0.321 0.313 0.252 0.279 1.800 0.000 0.246 0.263 0.358 0.364 0.361 0.000 0.000 0.000
6 IR1 piece5 13.000 100.000 0.807 0.433 0.564 0.028 0.172 0.048 0.113 0.095 0.103 37865.940 0.000 0.433 0.105 0.063 0.135 0.086 0.000 0.000 0.000
7 PLM1 piece1 5 4 0.839 0.372 0.515 0.860 0.295 0.439 0.399 0.190 0.258 14436.000 0.000 0.372 0.399 0.775 0.287 0.419 0.000 0.000 0.000
7 PLM1 piece2 5 6 0.242 0.236 0.239 0.000 0.000 0.000 0.194 0.199 0.196 103741.000 0.000 0.122 0.138 0.552 0.276 0.368 0.000 0.000 0.000
7 PLM1 piece3 10.000 3 0.463 0.225 0.303 0.000 0.000 0.000 0.537 0.275 0.364 129.000 0.000 0.225 0.537 0.549 0.500 0.524 0.000 0.000 0.000
7 PLM1 piece4 8 2 0.750 0.254 0.380 1.000 1.000 1.000 0.734 0.208 0.325 411.000 0.000 0.254 0.734 0.719 0.651 0.683 0.500 0.125 0.200

download these results as csv

Table 4. Tabular version of Figures 16-25.


AlgIdx AlgStub Piece n_P R_est R_occ(c=.75) R_occ(c=.5)
1 DM1 piece1 5
0.882 0.900 0.500 0.450 0.867
0.866 0.613 0.000 0.000 0.778
0.866 0.613 0.000 0.000 0.778
1 DM1 piece2 5
0.444 0.875 0.500 0.908 0.927
0.00000 0.802 0.000 0.908 0.923
0.00000 0.802 0.000 0.908 0.923
1 DM1 piece3 10.000
0.455 0.605 0.767 0.364 0.500 0.250 0.045 0.562 0.417 0.333
0.00000 0.000 0.678 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.678 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 DM1 piece4 8
1.00000 0.857 0.385 0.444 0.867 0.350 0.250 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
1 DM1 piece5 13.000
0.560 0.120 0.967 0.984 0.857 0.172 0.130 0.333 0.122 0.675 0.636 0.938 0.752
0.00000 0.000 0.967 0.949 0.711 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.752
0.00000 0.000 0.967 0.949 0.711 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.752
2 DM2 piece1 5
1.00000 1.000 0.125 0.000 0.682
0.866 0.613 0.000 0.000 0.000
0.866 0.613 0.000 0.000 0.000
2 DM2 piece2 5
0.056 0.348 0.021 0.876 0.419
0.00000 0.000 0.000 0.875 0.000
0.00000 0.000 0.000 0.875 0.000
2 DM2 piece3 10.000
0.349 0.612 0.619 0.163 0.500 0.250 0.045 0.562 0.146 0.083
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 DM2 piece4 8
0.667 0.538 0.625 0.571 0.469 0.643 0.450 0.591
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 DM2 piece5 13.000
0.094 0.092 0.350 0.273 0.254 0.122 0.051 0.069 0.084 0.462 0.243 0.938 0.515
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.926 0.000
3 DM3 piece1 5
0.882 0.857 0.333 0.273 0.867
0.866 0.613 0.000 0.000 0.778
0.866 0.613 0.000 0.000 0.778
3 DM3 piece2 5
0.800 0.438 0.333 0.672 0.920
0.739 0.000 0.000 0.000 0.913
0.739 0.000 0.000 0.000 0.913
3 DM3 piece3 10.000
0.600 0.674 0.722 1.000 0.579 0.355 0.065 0.795 1.000 0.667
0.00000 0.000 0.000 0.190 0.000 0.000 0.000 0.751 0.667 0.000
0.00000 0.000 0.000 0.190 0.000 0.000 0.000 0.751 0.667 0.000
3 DM3 piece4 8
1.00000 0.857 0.385 0.364 0.875 0.619 0.600 0.857
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
0.750 0.643 0.000 0.000 0.867 0.000 0.000 0.846
3 DM3 piece5 13.000
0.591 0.500 0.846 0.980 0.281 0.145 0.067 0.075 0.083 0.835 0.286 0.936 0.742
0.00000 0.000 0.846 0.947 0.000 0.000 0.000 0.000 0.000 0.504 0.000 0.924 0.000
0.00000 0.000 0.846 0.947 0.000 0.000 0.000 0.000 0.000 0.504 0.000 0.924 0.000
4 VM1 piece1 5
0.944 1.000 1.000 0.364 0.938
0.775 0.771 0.784 0.000 0.782
0.775 0.771 0.784 0.000 0.782
4 VM1 piece2 5
0.800 0.812 0.600 0.936 0.934
0.614 0.734 0.000 0.936 0.934
0.614 0.734 0.000 0.936 0.934
4 VM1 piece3 10.000
0.647 0.752 0.846 1.000 0.579 0.314 0.500 0.742 1.000 1.000
0.00000 0.752 0.724 0.831 0.000 0.000 0.000 0.000 0.905 1.000
0.00000 0.752 0.724 0.831 0.000 0.000 0.000 0.000 0.905 1.000
4 VM1 piece4 8
1.00000 0.857 0.600 1.000 1.000 1.000 0.900 1.000
0.860 0.429 0.000 1.000 1.000 0.912 0.750 1.000
0.860 0.429 0.000 1.000 1.000 0.912 0.750 1.000
4 VM1 piece5 13.000
0.857 0.714 0.989 0.993 0.968 0.400 0.125 0.529 0.500 0.771 0.800 0.668 0.573
0.857 0.000 0.989 0.993 0.968 0.000 0.000 0.000 0.000 0.366 0.746 0.000 0.000
0.857 0.000 0.989 0.993 0.968 0.000 0.000 0.000 0.000 0.366 0.746 0.000 0.000
5 VM2 piece1 5
0.727 0.719 0.625 0.111 0.667
0.00000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000
5 VM2 piece2 5
0.538 0.812 0.600 0.920 0.934
0.00000 0.734 0.000 0.920 0.934
0.00000 0.734 0.000 0.920 0.934
5 VM2 piece3 10.000
0.786 0.744 0.962 0.278 0.395 0.360 0.250 0.677 0.571 0.190
0.384 0.000 0.498 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.384 0.000 0.498 0.000 0.000 0.000 0.000 0.000 0.000 0.000
5 VM2 piece4 8
0.667 0.571 0.600 1.000 1.000 0.714 0.800 0.769
0.00000 0.000 0.000 0.911 1.000 0.000 0.733 0.769
0.00000 0.000 0.000 0.911 1.000 0.000 0.733 0.769
5 VM2 piece5 13.000
0.609 0.087 0.974 0.982 0.633 0.600 0.333 0.111 0.084 0.800 0.667 0.652 0.488
0.00000 0.000 0.974 0.982 0.000 0.000 0.000 0.000 0.000 0.388 0.000 0.000 0.000
0.00000 0.000 0.974 0.982 0.000 0.000 0.000 0.000 0.000 0.388 0.000 0.000 0.000
6 IR1 piece1 5
0.765 0.684 0.714 0.750 0.600
0.074 0.000 0.000 0.375 0.000
0.074 0.000 0.000 0.375 0.000
6 IR1 piece2 5
0.727 0.812 0.600 0.024 0.154
0.00000 0.109 0.000 0.000 0.000
0.00000 0.109 0.000 0.000 0.000
6 IR1 piece3 10.000
0.647 0.062 0.742 0.833 0.237 0.474 0.250 0.839 0.857 0.750
0.00000 0.000 0.000 0.144 0.000 0.000 0.000 0.468 0.238 0.188
0.00000 0.000 0.000 0.144 0.000 0.000 0.000 0.468 0.238 0.188
6 IR1 piece4 8
0.600 0.429 0.000 0.800 0.133 0.143 0.778 0.692
0.00000 0.000 0.000 0.177 0.000 0.000 0.389 0.000
0.00000 0.000 0.000 0.177 0.000 0.000 0.389 0.000
6 IR1 piece5 13.000
0.500 0.625 0.015 0.081 0.333 0.364 0.700 0.889 0.800 0.359 0.652 0.134 0.181
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.136 0.198 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.136 0.198 0.000 0.000 0.000 0.000
7 PLM1 piece1 5
0.941 0.842 0.077 0.000 0.000
0.309 0.265 0.000 0.000 0.000
0.309 0.265 0.000 0.000 0.000
7 PLM1 piece2 5
0.333 0.552 0.125 0.116 0.053
0.00000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000
7 PLM1 piece3 10.000
0.312 0.357 0.553 0.146 0.474 0.344 0.062 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
7 PLM1 piece4 8
0.250 0.500 0.000 0.286 0.000 1.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 1.000 0.000 0.000
0.00000 0.000 0.000 0.000 0.000 1.000 0.000 0.000
7 PLM1 piece5 13.000
0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263 0.263
0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054
0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054

download these results as csv

Table 5. Tabular version of Figures 14 and 15.

References

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  • Arnold Schoenberg. (1967). Fundamentals of Musical Composition. Faber and Faber, London.
  • Gissel Velarde and David Meredith. (2014). Submission to MIREX Discovery of Repeated Themes and Sections. 10th Annual Music Information Retrieval eXchange (MIREX'14), Taipei, Taiwan.
  • Cheng-i Wang, Jennifer Hsu, and Shlomo Dubnov. (2015a). Submission to MIREX Discovery of Repeated Themes and Sections. 11th Annual Music Information Retrieval eXchange (MIREX'15), Malaga, Spain.
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