Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Symbolic Textural Features
and Melody/Accompaniment Detection
in String Quartets
Louis Soum-Fontez1 , Mathieu Giraud2 ,Nicolas Guiomard-Kagan1 , and Florence Levé1,2
12

Université de Picardie Jules Verne, MIS, F-80000 Amiens, France
Université de Lille, UMR CNRS 9189 CRIStAL, F-59000 Lille, France
florence.leve@algomus.fr 

Abstract. Music is often described as melody and accompaniment, and several
MIR studies try to identify melodies. But the organization of voices is not limited to such a distinction between melody and accompaniment: Textural effects– such as repeated notes, syncopes, homorhythmy, parallel moves or imitation– underline the melody/accompaniment layout, and changes in texture usuallymark structural transitions in music. We investigate how textural and other characteristics can help to identify melodic voices in polyphonic music. We selectmeasure-level features to analyze symbolic scores of string quartets, including
new textural features, and propose models to predict, on each measure, melodic
and accompaniment layers in such scores, each layer possibly including several
instruments. We evaluate these sets of features and the models on 12 movements
in Haydn and Mozart string quartets. The best models have an average accuracyof more than 85%, taking into account both statistical and textural features.

1

Introduction

Melody, as the foreground of a musical material, is complex to define and characterize.
In string quartets, the first violin (Vln1), as a leading instrument, often plays the main
melody. However, the three other instruments of the quartet – second violin, viola, andcello – can also join the melody or play it alone over time (Figure 1).
Melody Detection. Research on melody extraction is an active field in the audio domain [23, 8, 14]. In the symbolic domain, studies investigated the melodic content ofmonophonic phrases and patterns through the lens of melodic similarity [31], melodic
segmentation [1, 19, 29, 30], and contour analysis [25, 24].
Concerning the particular question of identifying the melody in a polyphonic score,
Uitdenbogerd and Zobel [28] proposed several algorithms identifying the melodic line
in polyphonic MIDI files, including the simple skyline algorithm that labels as melody
the highest pitch at each onset. Rizo et al. proposed a set of statistical descriptors extracted from each track of MIDI files of different music styles (classical, jazz and pop)
and trained a random forest classifier to identify melody tracks in these pieces [22].
This work is partially funded by French CPER MAuVE (Région Hauts-de-France).

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

19-20: mel / acc/ acc
Vln1 / Vln2,Vla / Vla

21-22: mel/ acc / acc
Vln2,Vla / Vln1 / Vc

Fig. 1. Haydn, Quatuor op. 33/1, I, mes. 19-22. The texture is described for each measure – even
if the melodies are not stricty aligned on measures boundaries. On mesures 21-22, the role of thefirst violin may be debated, but the main melody is played on the second violin and on the viola,mostly in parallel move in sixths.

Madsen et al. proposed an algorithm for predicting melody notes at any point of thepiece, based on a sliding window rendering the complexity of the musical lines [17].
One limitation is that they assume that there is only one melodic line at a time and theyreported that the skyline algorithm was still better performing on one Haydn string quartet. They also use this method to identify the melody track in two datasets of popular
music [16]. These first results show that assessing complexity may help the recognitionof the melody. Friberg et al. tried to recognize the main melody in a polyphonic symbolic score on ringtones of popular music [7], using Huron’s perceptual principles [11].
Some of the features they use are derived from symbolic data but intend to model audio
features, such as timbre, staccato/legato, or sound level.
Texture and Melody. The role of texture has long been recognized in music theories[15, 13], but systematic, formalized, or computed analyses of texture remain few. In
1960, Nordgren quantified some aspects of the orchestral texture [20]. In 1982, Rahn,
discussing the melody identification in polyphonies, argues that a melody “stands out”
from its accompanying parts largely on the basis of its complexity [21]. In 1989, Huron
discussed the semantics of the term “texture” and proposed measures to evaluate thetextural diversity of music [10].
Several studies focused on the segregation of polyphonic music voices or streams, as
a listener might perceive them [2, 3, 26]. Duane’s thesis [6] further proposed to characterize texture in string quartets by grouping the notes in streams perceived by listeners
and by characterizing the role of these streams. He described three roles: main lines(including melodies), secondary lines and accompaniment. He established by statisticalmethods that the perception of textural flows was mainly related to note synchronicity,
coordinated pitch modulation (especially parallel movements), as well as the presence

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

of certain harmonic intervals (metricity, rhythmic repetition, rhythmic patterns, melodic
contour, and harmony do not seem to have a significant role).
We previously proposed to describe texture with layers, each one determined according to its role and qualified according to its composition [9]. At the first level, layers are mainly qualified as melody, accompaniment, or other minor roles. One melodic
layer may include several instruments, and there can be two melodic layers. At thesecond level, layers can be tagged as repeated notes, syncopation, sustained notes, imitation, and homorhythmy, which can be refined by a complementary descriptor in thecase of parallelism, unison, or octave.
Outline. Several textures are more specifically used for rhythmic or accompanimentparts (as repetitions, homorhythmies, or syncopes) or are indicators of some relationship between two voices or more. However, no MIR studies have linked such textures
to the analysis of melodic and accompaniment layers. Our goal here is to improve theanalysis of textures in symbolic scores, notably the melody/accompaniment detection,
focusing on string quartets, where melody is often taken by other instruments than
the first violin. We aim to improve the understanding of interactions between instruments and the changes in texture by determining melodic and accompaniment layers
more precisely. We select measure-level features to analyze symbolic scores of string
quartets, gathering existing features [22], and new textural features (Section 2). We introduce models to predict melodic and accompaniment layers based on such features(Section 3). We evaluate these sets of features and the models on a set of 12 movements
in Haydn and Mozart string quartets and discuss these results (Section 4).

2

Measure-level Features for Melody Detection

To predict whether a measure is melody or accompaniment, we use the following set offeatures computed on each measure.
2.1

Voice Name (4)These features enable the (baseline) skyline algorithm, considering that the top voice isthe melody.
– (voice-name): 4 binary features, activated depending on the voice (first violin, second violin, viola, cello)
2.2

Statistical Features (20)

The features introduced by Rizo et. al were used to predict, on the whole piece, which
track is the melody between all tracks [22]. They are linked to music properties thatcan make what is a melody or what is an accompaniment (see Section 4.2). We computed here these features on each measure. They are grouped in 5 categories: track
information (normalized duration, number of notes, occupation rate, polyphony rate),

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021
String Quartet in B Minor
1
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r r r r r r rFig. 2. Detection of individual textures in measures 19-23 of String Quartet op. 33-1, 1st movement by Haydn (see also Figure 1). The colors show the related voices for textures i/h/p.

pitch (highest, lowest, mean, standard deviation), pitch intervals (number of different
intervals, largest, smallest, mean, standard deviation), note durations (longest, shortest, mean, standard deviation), and syncopation (number of syncopated notes). We also
added the number of repeated notes.
2.3

Textural Features (7 × 16 + 1)

To further describe the music texture, we propose a new set of high-level features describing the organization of notes and voices. The taxonomy of [9] introduced several
textures but proposed an algorithm only for homorhythmic layers. Inspired by this taxonomy, we design here the following binary features, that can be computed on everynote:– repeated notes (r): We consider as repeated notes sequences of at least three successives notes of the same pitch and the same duration, for a total duration of at least
one beat and a half, possibly spaced with rests of at most one beat.
– syncopes (s): A note is considered as a syncope if it starts on a weak beat or on asecond half of any beat and continues on at least the next beat.
– homorhythmy (h): Two voices are considered as homorhythmic when they play
only notes starting and ending at the same time during at least three beats.
– parallel moves (p): Two homorhythmic voices are considered in a parallel movewhen at least three close pairs of notes have the same diatonic interval – generally
thirds, sixths, or octaves or unison.
– imitation (i): We consider as imitation the repetition of a pattern on some voice,(c) 1994, 2000 Center for Computer Assisted Research in the Humanities
called Copyrightthe original
pattern, by another voice with some delay. This can be seenMusic engraving by LilyPond 2.22.0—www.lilypond.org
as a parallel move delayed in time. This is computed by a simplification of theMongeau-Sankoff algorithm [18] requiring here five exact notes or more approximate matches.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

– rest (rest): There is no note, but a rest.
– The feature (none) is added when none of the previous six features is activated withthe given sixteenth.
On a given measure, these 7 features are actually computed on each of the 16 sixteenth notes – the considered corpus being in 4/4, see next section. For each sixteenth
note, a vector gives the features that are activated, taking into account an expansion rule– that is including notes that are still sounding but not attacked there. Moreover, thefollowing summarizing feature is added on each measure:– texture ratio: number of sixteenths on which at least one of the textures is activated,
represented as a ratio between 0 and 1
Figure 2 shows an example of the heuristic detection of these textures. The prediction of repeated notes (r) (such as some eights in measure 23) and homorhythmy (h) isvery reliable and would be here close to a manual annotation. The parallel move (p) is
correctly identified at measures 21-22. The imitation pattern (i) at measures 19-20 on
the first violin, that is later taken on the second violin and viola at measure 21-22, isalso correctly detected. However, a manual annotation would probably not set the same
boundaries for such annotations, for example by ending the homorythmy one note lateron the measure 20.

3

Learning Models: Melody/Accompaniment Prediction
as a Measure Classification Task

We see the melody/accompaniment prediction as a binary classification problem, given
the features presented in the previous section. We choose the measure granularity to beconsistent with the reference annotations. The statistical features were normalized into
a gaussian distribution (0 ± 1) and almost all the textural features are binary. Theses
vectors are gathered into a vector of maximal size 4 + 20 + (7 × 16 + 1) for eachmeasure, and a given melody or accompaniment class for the reference annotation.
3.1

Model Architecture

Two models were tested:– A random forest (RF) classifier as used by [22], taking the average of a set of
200 decision trees trained on random subsets of features, where data are weighted
to account for the unbalanceness of categories.
– A simple neural network (NN) with an initial dropout layers with a rate of 0.5 toreduce overfitting [27], 2 hidden fully connected linear layers (64 then 32), separated by relu activation layers and batch normalization layers, and a last layer,
composed of a unique neuron, with a sigmoid activation function and a thresholdof 0.5 between melody/accompaniment prediction. Weights were initialized uniformly. Batch size is 32 and the learning rate is 10−3 , with early stopping after
50 iterations without improvement. To estimate errors at each iteration, the loss
function used is binary cross-entropy. The optimization of the gradients is donewith Adam [12].

179Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Haydn
m % mel % Vln1 mel
17.1 i E Major 111 29.1
97.1
17.2 i F Major 101 30.9
81.6
17.3 iv Eb Major 70 34.1
73.1
33.1 i B minor 91 31.2
72.6
33.2 i Eb Major 90 34.9
93.5
33.3 i C Major 165 32.2
100.0
33.4 i Bb Major 83 35.7
98.3
50.1 i Bb Major 164 31.4
76.2

Mozartm % mel % Vln1 mel
No. 2, K. 155 i D Major 119 37.0
96.5
No. 4, K. 157 i C Major 126 45.6
80.8
No. 6, K. 159 i Bb Major 71 53.2
98.2
No. 14 K. 387 i G Major 171 29.5
86.7

Table 1. The corpus contains 12 movements of Haydn and Mozart string quartets, all in 4/4.
The last three columns give the number of measures (m), the ratio of measures (on the 4 voices)
labeled as melody, and the ratio of measures on the first violin labeled as melody.

4

Results

4.1

Corpus, Implementation, and Availability

The corpus includes 12 movements of string quartets by Haydn and Mozart (Table 1),
totaling 1362 measures, as .krn files. We extended the corpus of our previous study [9],and we distribute the complete set of annotations as open data at www.algomus.fr/
data. Each measure on each of the four instruments was labeled as melody, accompaniment, or other. Only measures with melody and accompaniment were taken into
account, totaling 4791 measures. The files were processed with music21 [5], using actual pitch spelling (for example to compute intervals), and the learning models were
implemented with keras [4]. The code is available at www.algomus.fr/code.
4.2

Statistics on the Features

Figure 3 shows the distribution of some features over the measures labeled as melody
or accompaniment in the corpus.
As expected, the features on the pitch (highest, mean, and lowest), being very
similar to (voice-name), are very significant to tell apart the melodic and accompaniment parts. More interestingly, other features play a significant role, such as (numnotes) (melody tends to have more notes) or (num-diff-int) (melody tends to use conjoint
intervals). In textural features, imitation and syncopes are significantly associated to
melody, whereas repeated notes are significantly associated with accompaniment. Homorhythmy and its subset parallel moves are found in both roles, but parallel moves are
more used for melody.
4.3

Accuracy over a Leave-one-piece-out Strategy

We did not split these 4791 measures into a training set and a validation/test set: Indeed,
having different measures of the same piece in different sets would bring overfitting due
to repeats or similar sections inside each piece. Considering the relatively small size of180

Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Highest_p
Mel
Acc

0.10

0.4

0.08

0.03

0.06

0.02

0.04

0.2

0.01

0.02
0.0

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1

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2

3

Num_diff_int

4

5
Mel
Acc

0.20
0.15

0.00

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5

10

15

20

Largest_int

25

30
Mel
Acc

0.15

0.05
2

4

6

8

10

12

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6%

imi
Mel
Acc

20%

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

rest

25%
20%

4

5

par
Mel
Acc

38%

65%
60%

homo

Mel
Acc

45%

5%

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

3

50%

10%

10%

2

55%

15%

15%

1

30%

Mel
Acc

20%

40%

0

32%

Mel
Acc

rep

0.0

34%

10%

2%

Mel
Acc

36%

15%

4%

25%

0 3 6 9 12 15 18 21 24 27

syn
Mel
Acc

Shortest_dur

0.8

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1
2
3
4
5
6
7
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9
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11
12
13
14
15
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0

30 40 50 60 70 80 90 100 110

0.4

0.10

0.00

0.00

0.6

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Mel
Acc

0.04

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

0.6

Num_notes

0.12

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

DurationMel
Acc

Fig. 3. Distribution of some features in the 12 movements of the corpus, split into the measureslabeled as melody (Mel) or accompaniement (Acc) in the reference analysis. The data was normalized in order that each of the area equals to 1. Top two lines: Some of the statistical features
introduced by [22]. Bottom two lines: Textural features on each sixteenth on each measure, temporal barplot representing the proportions of a given sixteenth having one of these textures.

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Features
MajorityTop (Vln1)
Statistics
TextureStatistics + Texture
V. NameV. Name + Texture
V. Name + Statistics
V. Name + Statistics + Texture

base
all
dm
65.4 19.0
84.1 0.0
–
–
–
–
–
–
–
–
–
–
–
–
–
–

RF
all
–
–
78.9
69.1
80.3
84.3
83.1
83.3
84.3

NN
all
dm
–
–
–
–
81.9 51.9
72.3 52.8
82.4 49.9
84.2 0.0
86.8 26.6
84.1 25.4
85.3 32.0

Table 2. Mean accuracy of the baseline models (base), as well of the Random Forest (RF) and theneural network (MLP) on the leave-one-piece-out strategy and various sets of features, evaluatedon all measures but also on difficult measures (dm) i.e. where the first violin is not playing the
melody or another voice is playing it.

the corpus, we rather opted for a leave-one-piece-out strategy: Each piece is separately
considered as a validation set of a model trained on all other pieces. We iterate andreport the average accuracy over all the pieces.
As baseline models, we consider Majority (all measures are predicted as accompaniment) and Top (the first violin, as the top voice, is predicted as the melody – this isequivalent than only considering the (voice-name) feature). Table 2 shows that the bestmodel is the NN taking into account the voice names and our proposed textural features, with about 86.8% of correct predictions. As expected, the (voice-name) alone hassignificant results – and this is confirmed by the baseline Top(Vln1), 84.1%. The statistical features, even alone, have a good performance, but include features on pitch thatare very similar to (voice-name). Conversely, the textural features, without any feature
related to pitch, manage alone to identify 72.3% of the measures, including 52.8% on
difficult measures where the first violin is the voice playing the melody. Adding thesetextural features to (voice-name) improves the accuracy.
We call difficult measures the 15.8% measures where melody is not at the first violin or shared between several instruments. The best model here correctly predicts the
melody in 32% of such measures.
4.4

Focus on Specific Cases

With the best model, the best results are on the first movement of Haydn 33.4 (96.1%),
this movement having melodies almost always played on the first violin (see Table 1).
Conversely, Figure 4 details the prediction on five difficult measures in a Mozart
quartet. On measures 27-29, the three voices are predicted as accompaniment, whereas
the reference annotation labels as melody the second violin. Although the melodic patterns are about the same in measures 27, 30, and 31, it is worth noting that the prediction
on measure 27 is wrong, whereas on the measures 30 and 31, the model correctly predicts the melody in parallel moves (p) between one of the violins and the viola: The
textural information here helps in predicting the melody.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021
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Fig. 4. Textural features, reference annotation (top), and mel/acc prediction by the model NN(bottom) on measures 27 to 31 from quatuor K387 by Mozart, first movement.

5

Conclusion and Perspectives

We evaluated sets of features and models to predict, on each measure, which instrument(s) is playing a melodic content. Experiments on string quartets by Haydn andMozart show that some textural features are distributed differently in melodic and accompaniment parts, and that the best models detect some of the melodies beyond thefirst violin or distributed among several instruments. This brings a new step towards
a general characterization of melody and texture in polyphonic pieces. Further studies could improve the features and the learning model, generalize such approaches tomore complex polyphonic works such as orchestral music, and study the correlation of
texture with other parameters such as harmony or form.

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