2019:Audio Chord Estimation Results

Introduction

This page contains the results of the 2018 edition of the MIREX automatic chord estimation tasks. This edition was the sixth one since the reorganization of the evaluation procedure in 2013. The results can therefore be directly compared to those of the last five years. Chord labels are evaluated according to five different chord vocabularies and the segmentation is also assessed. Additional information about the used measures can be found on the page of the 2013 edition.

What’s new?

• All datasets and evaluation procedures are the same as last year's MIREX.

Software

All software used for the evaluation has been made open-source. The evaluation framework is described by Pauwels and Peeters (2013). The corresponding binaries and code repository can be found on GitHub and the used measures are available as presets. The raw algorithmic output will be provided later More help can be found in the readme.

The statistical comparison between the different submissions is explained in Burgoyne et al. (2014). The software is available at BitBucket. It uses the detailed results provided below as input.

Submissions

	Abstract	Contributors
CM1	PDF	Chris Cannam, Matthias Mauch
JLCX1, JLCX2	PDF	Junyan Jiang, Ke Chen, Wei Li, Guangyu Xia
SG1	PDF	Franz Strasser, Stefan Gaser
FK2	[PDF]	Florian Krebs, Filip Korzeniowski, Sebastian Böck

Results

Summary

All figures can be interpreted as percentages and range from 0 (worst) to 100 (best).

Isophonics2009

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	79.89	76.50	74.33	67.79	65.82	82.61	81.56	85.11
CM1	78.66	75.51	72.58	54.78	52.36	85.87	87.22	85.98

download these results as csv

Billboard2012

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	76.32	73.38	71.60	63.59	61.92	82.05	81.74	83.77
CM1	74.23	72.31	70.25	55.44	53.48	83.60	85.33	83.31

download these results as csv

Billboard2013

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	73.69	69.47	67.66	57.41	55.78	78.80	78.67	82.52
CM1	71.23	67.36	65.28	49.07	47.25	81.50	83.13	82.53

download these results as csv

JayChou29

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	65.44	62.49	55.27	46.83	40.60	78.95	80.78	77.55
CM1	72.82	72.15	65.55	54.46	49.05	86.58	86.94	86.84

download these results as csv

RobbieWilliams

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	79.89	75.45	73.73	68.57	66.91	83.60	83.72	84.32
CM1	81.94	78.29	76.09	57.97	55.94	87.95	89.00	87.39

download these results as csv

RWC-Popular

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	70.33	66.77	63.42	56.09	52.97	79.26	79.17	80.14
CM1	79.12	77.95	74.30	63.33	59.89	88.62	88.11	89.67

download these results as csv

USPOP2002Chords

Algorithm	MirexRoot	MirexMajMin	MirexMajMinBass	MirexSevenths	MirexSeventhsBass	MeanSeg	UnderSeg	OverSeg
CLSYJ1	76.23	72.30	68.75	62.27	58.92	82.13	82.12	83.24
CM1	78.41	76.24	72.74	59.84	56.67	85.81	86.81	86.02

download these results as csv

Detailed Results

More details about the performance of the algorithms, including per-song performance and supplementary statistics, are available from ?.

Algorithmic Output

The raw output of the algorithms are available in ?. They can be used to experiment with alternative evaluation measures and statistics.