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Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music Cover

Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music

Open Access
|Jan 2019

Figures & Tables

tismir-2-1-18-g1.png
Figure 1

Segment-based recognition (top) vs. event-based recognition (bottom) on measures 11 and 12 from Beethoven WoO68, using note onsets and offsets to create event boundaries.

Table 1

Input representation for measure 12 from Beethoven WoO68, showing the pitches and duration for each event, as well as the corresponding segment and label, where G7 stands for G:maj:add7, and C stands for C:maj.

Seg.LabelEventPitchesLen.
s1G7e1G3, B3, D4, G51/8
G7e2G3, B3, D4, F51/8
G7e3B4, D53/16
G7e4B4, D51/16
s2Ce5C4, C5, E51/8
Ce6G3, C5, E51/8
Ce7E3, G4, C5, E51/8
Ce8C3, G4, C5, E51/8
tismir-2-1-18-g2.png
Figure 2

Segment and labels (top) vs. events (bottom) for measure 12 from Beethoven WoO68.

tismir-2-1-18-g3.png
Figure 3

Factor graph representation of the semi-CRF.

Table 2

Dataset statistics and summary of results (event-level accuracy AccE and segment-level F-measure FS).

DatasetStatisticsFull chord evaluationRoot-level evaluation
semi-CRFHMPerceptronsemi-CRFHMPerceptronMelisma
EventsSeg.’sLabelsAccEFSAccEFSAccEFSAccEFSAccEFS
BaCh5,6643,0909083.277.577.269.988.984.284.877.084.374.7
TAVERN63,87612,8026978.064.057.022.586.071.469.233.276.741.5
KPCorpus3,8889117673.053.072.945.479.359.079.051.981.962.2
Rock25,6214,2214870.155.961.334.686.165.180.742.977.936.3
Table 3

Comparative results (%) and standard deviations on the BaCh dataset, using Event-level accuracy (AccE) and Segment-level precision (PS), recall (RS), and F-measure (FS).

BaCh: Full chord evaluation
SystemAccEPSRSFS
semi-CRF83.2
0.2
79.4
0.2
75.8
0.2
77.5
0.2
HMPerceptron177.2
2.1
71.2
2.0
68.8
2.2
69.9
1.8
HMPerceptron277.0
2.1
71.0
2.0
68.5
2.3
69.7
1.8
Table 4

Root only results (%) on the BaCh dataset, using Event-level accuracy (AccE) and Segment-level precision (PS), recall (RS), and F-measure (FS).

BaCh: Root only evaluation
SystemAccEPSRSFS
semi-CRF88.985.483.084.2
HMPerceptron84.878.076.277.0
Melisma84.373.276.374.7
Table 5

Full chord Event (AccE) and Segment-level (PS, RS, FS) results (%) on the BaCh dataset, with and without metrical accent features.

BaCh: Metrical accent evaluation of semi-CRF
SystemAccEPSRSFS
With accent83.679.675.977.6
Without accent77.774.868.071.2
Table 6

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the TAVERN dataset.

TAVERN: Full chord evaluation
SystemAccEPSRSFS
semi-CRF78.067.360.964.0
HMPerceptron57.024.520.822.5
Table 7

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the TAVERN dataset.

TAVERN: Root only evaluation
SystemAccEPSRSFS
semi-CRF86.074.668.471.4
HMPerceptron69.238.229.433.2
Melisma76.742.340.741.5
tismir-2-1-18-g4.png
Figure 4

Semi-CRF correctly predicts A:maj7 (top) for the first beat of measure 55 from Mozart K025, while HMPerceptron predicts C#:dim (bottom).

tismir-2-1-18-g5.png
Figure 5

Semi-CRF correctly predicts C:maj (top) for all of measure 280 from Mozart K179, while HMPerceptron predicts E:min (bottom) for the first beat and C:maj for the other two beats (bottom).

Table 8

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the KP Corpus dataset.

KP Corpus 46 excerpts: Full chord evaluation
SystemAccEPSRSFS
semi-CRF172.059.049.253.5
semi-CRF273.459.650.154.3
Table 9

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the KP Corpus dataset.

KP Corpus 46 excerpts: Root only evaluation
SystemAccEPSRSFS
semi-CRF80.766.356.260.8
Melisma80.960.663.361.9
Table 10

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the KP Corpus dataset.

KP Corpus 36 excerpts: Full chord evaluation
SystemAccEPSRSFS
semi-CRF73.055.650.753.0
HMPerceptron72.948.243.645.4
Table 11

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the KP Corpus dataset.

KP Corpus 36 excerpts: Root only evaluation
SystemAccEPSRSFS
semi-CRF79.361.856.459.0
HMPerceptron79.054.749.951.9
Melisma81.960.763.762.2
Table 12

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the Rock dataset.

Rock 59 songs: Full chord evaluation
SystemAccEPSRSFS
semi-CRF166.049.847.348.5
semi-CRF369.462.054.958.3
Table 13

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the Rock dataset.

Rock 59 songs: Root only evaluation
SystemAccEPSRSFS
semi-CRF85.870.963.266.8
Melisma77.429.544.035.3
Table 14

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the Rock dataset.

Rock 51 songs: Full chord evaluation
SystemAccEPSRSFS
semi-CRF70.158.853.255.9
HMPerceptron61.341.029.934.6
Table 15

Event (AccE) and Segment-level (PS, RS, FS) results (%) on the Rock dataset.

Rock 51 songs: Root only evaluation
SystemAccEPSRSFS
semi-CRF86.168.661.965.1
HMPerceptron80.751.336.942.9
Melisma77.930.645.836.3
tismir-2-1-18-g6.png
Figure 6

Measures 14–15 of ‘Let It Be’ by the Beatles, where HMPerceptron incorrectly predicts G:maj6 for measure 15 (bottom), while semi-CRF correctly predicts G:maj (top).

DOI: https://doi.org/10.5334/tismir.18 | Journal eISSN: 2514-3298
Language: English
Submitted on: Apr 27, 2018
Accepted on: Oct 22, 2018
Published on: Jan 3, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Kristen Masada, Razvan Bunescu, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.