2013:Audio Chord Estimation Results MIREX 2009

Introduction

This year, we have started a new evaluation battery for audio chord estimation. This page contains the results of these new evaluations for the Isophonics dataset, a.k.a. the MIREX 2009 dataset. It comprises the collected Beatles, Queen, and Zweieck datasets from Queen Mary, University of London, and has been used for audio chord estimation in MIREX for many years.

Why evaluate differently?

• Researchers interested in automatic chord estimation have been dissatisfied with the traditional evaluation techniques used for this task at MIREX.

• Numerous alternatives have been proposed in the literature (Harte, 2010; Mauch, 2010; Pauwels & Peeters, 2013).

• At ISMIR 2010 in Utrecht, a group discussed alternatives and developed the Utrecht Agreement for updating the task, but until this year, nobody had implemented any of the suggestions.

What’s new?

More precise recall estimation

• MIREX typically uses chord symbol recall (CSR) to estimate how well the predicted chords match the ground truth: the total duration of segments where the predictions match the ground truth divided by the total duration of the song.

• In previous years, MIREX has used an approximate CSR by sampling both the ground-truth and the automatic annotations every 10 ms.

• Following Harte (2010), we view the ground-truth and estimated annotations instead as continuous segmentations of the audio because (1) this is more precise and also (2) more computationally efficient.

• Moreover, because pieces of music come in a wide variety of lengths, we believe it is better to weight the CSR by the length of the song. This final number is referred to as the weighted chord symbol recall (WCSR).

Advanced chord vocabularies

• We computed WCSR with five different chord vocabulary mappings:

1. Chord root note only;
2. Major and minor;
3. Seventh chords;
4. Major and minor with inversions; and
5. Seventh chords with inversions.

• With the exception of no-chords, calculating the vocabulary mapping involves examining the root note, the bass note, and the relative interval structure of the chord labels.

• A mapping exists if both the root notes and bass notes match, and the structure of the output label is the largest possible subset of the input label given the vocabulary.

• For instance, in the major and minor case, G:7(#9) is mapped to G:maj because the interval set of G:maj, {1,3,5}, is a subset of the interval set of the G:7(#9), {1,3,5,b7,#9}. In the seventh-chord case, G:7(#9) is mapped to G:7 instead because the interval set of G:7 {1, 3, 5, b7} is also a subset of G:7(#9) but is larger than G:maj.

• Our recommendations are motivated by the frequencies of chord qualities in the Billboard corpus of American popular music (Burgoyne et al., 2011).

Most Frequent Chord Qualities in the Billboard Corpus

Quality	Freq.	Cum. Freq.
maj	52	52
min	13	65
7	10	75
min7	8	83
maj7	3	86

Evaluation of segmentation

• The chord transcription literature includes several other evaluation metrics, which mainly focus on the segmentation of the transcription.

• We propose to include the directional Hamming distance in the evaluation. The directional Hamming distance is calculated by finding for each annotated segment the maximally overlapping segment in the other annotation, and then summing the differences (Abdallah et al., 2005; Mauch, 2010).

• Depending on the order of application, the directional Hamming distance yields a measure of over- or under-segmentation. To keep the scaling consistent with WCSR values (1.0 is best and 0.0 is worst), we report 1 – over-segmentation and 1 – under-segmentation, as well as the harmonic mean of these values (cf. Harte, 2010).

Comparative Statistics

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Submissions

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Results

Summary

All figures can be interpreted as percentages and range from 0 (worst) to 100 (best). The table is sorted on WCSR for the major-minor vocabulary. Algorithms that conducted training are marked with an asterisk; all others were submitted pre-trained.

Algorithm	Root Only	MajMin	MajMin + Inv	Sevenths	Sevenths + Inv	Mean Seg	Under-Seg	Over-Seg
CB4*	84	82	79	69	66	89	86	92
KO1	83	82	80	76	73	88	86	91
CB3	84	82	79	69	66	89	87	92
NMSD2	83	81	79	69	66	88	86	90
NMSD1	83	81	78	66	64	87	85	90
KO2*	81	79	77	69	66	87	84	91
CF2	79	75	72	55	52	87	87	86
NG1	77	75	72	66	63	85	82	88
PP3	77	75	72	67	64	86	84	88
PP4	75	73	70	61	58	86	82	89
NG2	73	71	68	43	41	84	83	84
SB8	9	7	7	7	6	56	92	40

download these results as csv

Comparative Statistics

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Complete Results

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Algorithmic Output

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