Comparative Melodic Analysis of
A Cappella Flamenco Cantes
Juan J. Cabrera
Department of Applied Mathematics, School of Engineering, University of Seville, Spain
juacabbae@us.com
Jose Miguel Díaz-Báñez
Department of Applied Mathematics, School of Engineering, University of Seville, Spain
dbanez@us.es - http://www.personal.us.es/dbanez
Francisco J. Escobar-Borrego
Department of Audiovisual Communication, Publicity and Literature, University of Seville, Spain
fescobar@us.es
Emilia Gómez
Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain
egomez@iua.upf.edu - http://mtg.upf.edu/~egomez
Francisco Gómez
Applied Mathematics Department, School of Computer Science, Polytechnic Univ. of Madrid,
E-mail: fmartin@eui.upm.es
Joaquín Mora
Department of Evolutive and Educational Psychology, University of Seville, Spain
mora@us.es
In: C. Tsougras, R. Parncutt (Eds.)
Proceedings of the fourth Conference on Interdisciplinary Musicology (CIM08)
Thessaloniki, Greece, 2-6 July 2008, http://web.auth.gr/cim08/
Background in ethnomusicology and music analysis. A cappella singing styles (called cantes in the flamenco
jargon) are among the most fundamental song styles within the flamenco repertoire. Until very recently, flamenco
singers have been only using oral transmission to learn them. Because of this form of diffusion, melody has become
one of the main musical facets to be listened to, remembered, elaborated and spread in flamenco singing styles.
Moreover, melody has helped flamenco enthusiasts to remember and identify variants of a particular style or genre. A
frequent discussion and unanswered question among flamenco scholars is how to quantify the similarity between two
melodies, and how to use this similarity measure to differentiate different styles and variants among performers, and
to study the roots and evolution of flamenco styles.
Background in computing, mathematics and statistics. State of the art techniques in melodic analysis of audio
allow us to obtain different representation levels of a music recording (Gómez et alt. 2003). There are different
representation levels for melody. Energy (intensity) and fundamental frequency (pitch) curves are the main low-level
melodic features. In a higher structural level, note duration and pitch provide a symbolic representation, which can be
the input to higher-level analyses. Finally, deviations of the analyzed recording with respect to the obtained score are
related to expressivity. There have been some attempts to apply these techniques to the analysis of flamenco music
(Donnier 1997, Gómez and Bonada 2008). There also a corpus of research on how to measure similarity between
melodies using computational models, which are usually inspired in methods for string comparison (Crawford et alt.
1998, Mongeau and Sankoff 1990).
Aims. After some previous studies on rhythmic similarity of flamenco styles (Díaz-Bañez et alt. 2004), the aim of this
work is to compare different approaches of melodic and tonal analysis of flamenco a capella singing styles. The
ultimate goal is to perform a multidisciplinary analysis that will provide us with tools to compare different versions of
the same style, and, as a consequence, to clarify the roots of styles and their evolution. We contrast historical
knowledge, we carry out manual melodic and tonal analysis assisted with automatic melodic description tools. In order
to achieve this goal, it is also necessary to gather a representative musical corpus, a significantly difficult task due to
the variety of media (mostly vinyl and magnetic tapes) and poor quality of existing flamenco recordings.
Main contribution. One of the main contributions of this work is to gather a representative music collection for this
study. The corpus was found on very diverse media (mostly vinyl and magnetic tapes), and comprises recording from
the 40’s up to day. We have considered a music collection of songs without instrumentation or in some cases with
some percussion, known in flamenco music as cantes a palo seco. This corpus is composed of two main styles,
namely: martinetes and tonás. In total, there are 135 monophonic voice pieces covering the most representative
flamenco singers. Musicological and historical criteria were followed when selecting the pieces.
As a second step, we have performed different analysis of this music collection. From a musical and computational
perspective, we first define a melodic representation capable of coping with the relevant subtleties of this kind of style,
characterized by ornamentations. This melodic representation is automatically computed from the selected phrases
(Gómez and Bonada 2008). Critical issues have risen regarding the location of the main notes of the melody, the
idiosyncratic use of vibrato and the presence of non-equal-tempered intervals. After that, some similarity measures
between each pair of pieces have been computed, such as the edit distance (Gascuel 1997), in order to obtain a
similarity matrix for the studied corpus. We compared the obtained similarity matrix with a set of manual annotations
produced by flamenco experts; those manual annotations included perceptual melodic contour, tonal analysis as well
as historical data based on previous well-established facts and tenets about the analyzed corpus. The different
analyses are presented in the paper, and we will listen to some examples of the analyzed pieces in the conference
presentation.
Conclusions. Melody is one of the most important aspects to consider when analyzing a cappella flamenco singing
styles. Automatic melodic description tools have proven useful to the analysis of flamenco voices, notwithstanding the
traditional techniques of music analysis. These automatic tools allow us obtaining quantitative measures that can
complement historical data on the roots and the evolution of oral transmission styles.
Introduction
A cappella singing styles (called cantes or
palos in the flamenco jargon) are among the
main styles within the flamenco repertoire.
Until very recently, flamenco singers have
been only using oral transmission to learn
them. In oral transmission, melody has
become one of the main musical facets to be
listened to, remembered, elaborated and
spread in flamenco singing styles. Moreover,
melody has helped flamenco enthusiasts to
remember and identify variants of a particular
style or genre. A frequent discussion and also
unanswered
question
among
flamenco
scholars is how to quantify the similarity
between two melodies, and how to use this
similarity measure to differentiate different
styles and variants among performers, and to
study the roots and the evolution of flamenco
styles.
After some previous studies on rhythmic
similarity of flamenco styles (Díaz-Bañez et
alt.
2004),
we
intended
to
compare
complementary approaches for the melodic
and tonal analysis of flamenco a capella
singing styles. The aim of this work is then to
analyze flamenco singing melodies from
different perspectives, and use such analyses
to compare different versions of the same
style. This would eventually lead to a
clarification of the roots of styles and their
evolution. We provided some historical
information, manual melodic and tonal
analysis and automatic melodic description
tools for the analysis of a corpus of cantes.
We carried out a clustering analysis based on
similarity measures both for manual and
automatic descriptions.
In order to achieve this goal, it is also
necessary to gather a representative musical
corpus, a significantly difficult task due to the
variety of media (mostly vinyl and magnetic
tapes) and poor quality of existing flamenco
recordings.
A cappella flamenco Cantes
We consider a music collection made of songs
without instrumentation or in some cases with
some percussion, known in flamenco music as
cantes a palo seco. This corpus is composed
of tonás. Tonás are songs without any musical
accompaniment that in a generic form
encompass martinetes, deblas, saetas, tonás
and carceleras. In this paper, we concentrate
ourselves on two main styles, namely deblas
and martinetes.
A toná is a song with a ‘copla’ of verses of
either three or four or eight syllables, where
the second and the forth verses may have
assonant rhythm, which is usually finished
with an imperfect tercet (off-rhyme tercet).
Although its origin is uncertain, many people
in the flamenco world believe that the toná is
the mother of all flamenco styles (cante
madre), and that from it all other styles are
derived. Some researchers, honestly trying to
build a corpus of research but not counting on
a fully rigorous methodology, have dated its
origin in the XVIII century in Jerez and Triana
CIM08 - Conference on Interdisciplinary Musicology - Proceedings
(Molina and Mairena 1963), but more recent
studies propose a later appearance (Jaramillo
2002, among others). Many tonás have been
given a particular name along history,
especially due to the work of Molina and
Mairena. Many of those names are merely
names familiar to the singers, but they don’t
reflect any musical feature. In spite of this
fact, they speak about the toná del Cristo,
toná de los pajaritos, toná liviana de Tia
Sarvaora, toná de la Junquera, toná de
Jaunelo, etc.
The debla is a song that stems from the basis
of the toná. Its melody requires a melismatic
ornamentation, more abrupt than the rest of
the songs of the tonás group.
The martinete is also considered a variety of
the toná. It differs from the latter in lyrics
and its melodic model, which always finishes
in the major mode. It is usually a sad style
and
it
is
played
without
guitar
accompaniment,
as
the
tonás
group.
Nevertheless,
martinetes
are
usually
accompanied by the percussion of a mallet
struck against an anvil.
Singer
Antonio
Mairena
Chano
Lobato
Chocolate
Year
1960
J. Almadén
1985
Jesús
Heredia
M. Simón
2002
M. Vargas
1972
Naranjito
2002
Pepe de
Lucía
Talegón
1963
2002
Córdoba
Tomás Pabón
1950
Turronero
1989
Triana
(Sevilla)
Utrera
2002
1999
1985
Location
Mairena
del Alcor
Cádiz
Jerez de
la
Frontera
Ciudad
Real
Écija
School
Pabón
Pabón
Mairena
Pabón
Mairena
Pabón
Mairena
Pabón
Mairena
Pabón
Jerez de
la
Frontera
Cazalla de Mairena
la Sierra
Triana
Pabón
(Sevilla) Mairena
Algeciras
Pabón
Music collection
Pabón
Mairena
El
Baboso
Pabón
Mairena
Table 1. Set of analyzed pieces.
One of the main contributions of this work is
to gather a representative music collection for
this study. The corpus was found on very
diverse media (mostly vinyl and magnetic
tapes) and comprises recording from the 40’s
up to day. In total, there are 135 monophonic
voice pieces covering the most representative
flamenco singers. Musicological, geographical
and historical criteria were followed when
selecting the pieces. For instance, the singer
Tomás Pabón is known as the one who
established the debla style, and some
relevant contributors were Antonio Mairena
and Naranjito de Triana. These last two
artists are considered as the big masters from
the 80’s, and they influenced all the rest of
singers. Table 1 provides some details on this
music collection.
Singer
Antonio Mairena
Chano Lobato
Chocolate
J. Almadén
J. Heredia
M. Simón
M. Vargas
Naranjito
Pepe de Lucía
Talegón
Tomás Pabón
Turronero
Key
C Major
Bb Major
G Major
B Major
Bb Major
C Major
A Major
C Major
C# Major
C# Major
Bb Major
B Major
Table 2. Keys of the analyzed Deblas.
martinete and debla exhibit. From these 135
excerpts, we have started by selecting 12
singers with the most representative deblas
and martinetes.
The chosen pieces have been manually
segmented into phrases, and we have
selected the first phrase, as it provides the
main melodic theme. This choice has been
motivated by the rigidity of musical form that
Tonal analysis
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CIM08 - Conference on Interdisciplinary Musicology - Proceedings
12
10
10
8
6
8
4
2
6
0
4
-2
2
-4
0
-6
Figure 1-a. Manual transcription of a debla
performed by Antonio Mairena.
Figure 2-a. Manual transcription of a martinete
performed by Antonio Mairena.
12
10
8
6
4
2
0
-2
-4
-6
10
8
6
4
2
0
Figure 2-b. Manual transcription of a martinete
performed by Tomás Pabón.
Figure 1-b. Manual transcription of a debla
performed by Tomás Pabón.
and Naranjito follow Mairena’s version (in C
Major)?
A tonal analysis of the selected 12 deblas in
terms of global key and modulations is
provided in this section. The analyzed deblas
provides a modal context, according to the
following tonal structure: Tonic (I) Subdominant (IV) - Tonic (I). We observe a
wide range of keys at the analyzed pieces, as
shown in Table 2.
Manual melodic transcription
and clustering
A manual melodic analysis of our initial
corpus was made by a flamenco expert (with
formal musical training also).
According to its key, we identify 6 different
groups: pieces in G Major (Chocolate), A
Major (Vargas), Bb Major (Pabón, Lobato and
Heredia), B Major (Almadén and Turronero),
C Major (Mairena, Simón and Naranjito) and
C# Major (Pepe de Lucía and Talegón).
For this manual analysis we have first
normalized the melodic contour with respect
to the tonic, and notes have been
approximated to the closest note in the
equal-tempered scale. Then, ornamentations
or melisma have been removed according to
some previous knowledge of the particular
style under analysis.
We also observe the presence of microtonality
in the modulations; most of the times this is
due to tuning problems. In fact, the flamenco
singer is not trained to perform accurate
modulations
whatsoever,
at
least
in
traditional flamenco singing.
There is no straight rhythmic representation,
as manual annotation has been made
following perception of rhythmic pulses, and
not according to note durations.
The key is mostly related to the particular
tessitura of the singer, but we might wonder
if the chosen key could be also related to a
similarity
between
singing
styles.
For
instance, did Lobato and Heredia follow
Pabón’s version (in Bb Major)? Or did Simón
Figures 1-a and 1-b show graphical
representations of the main melodic contour
of a debla performed by Antonio Mairena and
Tomás Pabón respectively. The reader may
observe that there are short-time differences,
although the overall melodic contour is
similar.
4
CIM08 - Conference on Interdisciplinary Musicology - Proceedings
Figures
2-a
and
b
show
graphical
representations of the main melodic contour
of a martinete sung by Antonio Mairena and
Tomás Pabón, respectively. We also found
some short-time differences, although the
overall melodic contour is kept.
Computational analysis
A computational analysis is made in order to
automate the melodic transcription and use
alternative similarity distances proposed in
the literature.
With these manual patterns we have
performed a similarity study based on multidimensional scaling, by using the Euclidean
distance between the vector representations
of the sequential values of melodies. A twodimensional projection of the d-dimensional
data is presented in Figure 3. We observe
that martinetes (M) and deblas (D) are
located in different regions of the 2-D plot.
Note that martinetes from Mairena and Pabón
(Tomás) are close to each other, whereas
their deblas are very far away from each
other.
State of the art techniques in automatic
melodic analysis of audio allow us to obtain
different representation levels of a music
recording (Gómez et alt. 2003). There are
different representation levels for melody.
Energy (intensity) and fundamental frequency
(pitch) curves are the main low-level melodic
features. In a higher structural level, note
duration and pitch provide a symbolic
representation, which can be the input to
higher-level analyses. Finally, deviations of
the analyzed recording with respect to the
obtained score are related to expressivity.
There have been some attempts to apply
these techniques to the analysis of flamenco
music (Donnier 1997, Gómez and Bonada
2008).
5
CIM08 - Conference on Interdisciplinary Musicology - Proceedings
70
66
68
64
66
62
64
60
62
58
60
56
58
54
56
52
54
0
5
10
15
20
25
note index
30
35
40
45
50
50
0
5
10
15
20
25
note index
Figure 4-a. Automatic transcription of a debla
performed by Antonio Mairena.
Figure 5-a. Automatic transcription of a
martinete performed by Antonio Mairena.
70
66
68
64
66
62
64
60
62
58
60
56
58
54
56
52
54
50
0
5
10
15
20
25
note index
30
35
40
45
0
Figure 4-b. Automatic transcription of a
debla performed by Tomás Pabón.
5
10
15
note index
20
25
30
Figure 5-b. Automatic transcription of a
martinete performed by Tomás Pabón.
Figures 4-a and 4-b provide a graphical
representations of the extracted melodic
contour of a debla performed by Antonio
Mairena and Tomás Pabón respectively. We
observed some differences with respect to
manual annotations. Although there are some
errors in the automatic transcription due to
the lack of tuning, most of the differences are
due to simplifications and assumptions made
when manually labeling the melodies (i.e.
melisma are not considered, note durations
are neglected and only the main melodic
anchor points are notated).
Automatic melodic transcription
We have tested two methods for automatic
melodic analysis (Gómez and Bonada 2008,
Leman et alt. 2003), which allow us to obtain
a MIDI-like representation of singing melodies
(onsets, offsets and frequency of every pitch
event). It’s important to note that each singer
has its own reference tone in mind and
he/she sings each note relatively to the scale
constructed on that tone (Haus and Pollastri,
2001). It is then necessary to estimate this
tuning frequency by dividing the semitone
into ten overlapping bins, each one being 0.2
semitones wide with an overlapping region of
0.1 semitones. The mean of the deviations
that belong to the maximum bin is the
constant average distance in semitones from
the user's reference tone. Thus, the scale can
be shifted by this estimated amount.
Once we had every piece converted into MIDI
notes, we needed to normalize it with respect
to the key, so that the melodic representation
is invariant to transposition. In order to
obtain this, we compute the intervals
between consecutive pitches, instead of
working with the absolute pitch values.
6
CIM08 - Conference on Interdisciplinary Musicology - Proceedings
Figure 5. A SplitTree for 12 Deblas and 12 Martinetes.
Besides, we wanted the representation of the
pieces not to be affected by tempo. We then
normalized note durations with respect to the
duration of the previous note.
The first one is the correlation coefficient
between note histograms. We measured how
interval
distributions
correlate
without
considering its ordering. Correlation indicates
the strength and direction of a linear
relationship between two random variables.
We obtained this coefficient for each pair of
pieces, resulting in a similarity matrix.
Measures for similarity computation
There is a vast corpus of research on how to
measure similarity between melodies using
computational models, usually inspired in
methods for string comparison (Crawford et
alt. 1998, Mongeau and Sankoff 1990).
The second measure considered was the
distance between the note sequences: the
edit distance (Mongeau and Sankoff 1990). It
is a metric that counts the difference between
two sequences under certain operations. The
edit distance between two strings is given by
the minimum number of operations needed to
transform one string into the other, where an
operation is an insertion, deletion, or
substitution of a single note. Both pitch and
note duration information were considered in
We implemented several melodic similarity
measures. As a preliminary study, we have
computed some of them so far to find out
which
measure
would
be
the
most
appropriated for our study. For the time
being, we have dealt with two different
measures.
7
CIM08 - Conference on Interdisciplinary Musicology - Proceedings
the algorithm, as they increases the
discrimination (López and Rocamora 2005).
Díaz-Báñez J. M., G. Farigu, F. Gómez, D.
Rappaport, G. T. Toussaint (2004). El Compás
Flamenco: A Phylogenetic Analysis. Proceedings of
BRIDGES: Mathematical Connections in Art, Music,
and Science, Winfield, Kansas, pages 61-70.
Phylogenetic trees
In
addition
to
similarity
analysis,
a
phylogenetic analysis is made in order to
study relationships among the transcribed
performances.
Donnier, P (1997). Flamenco: elementos para la
transcripción del cante y la guitarra. III Congress
of the Spanish Ethnomusicology Society.
Several techniques exist for generating
phylogenetic trees from distance matrices.
Once we computed the different similarity
matrixes, we generated a phylogenetic tree
using the tool SplitsTree (Huson 1998).
SplitsTree computes a tree with the property
that the distance in the drawing between any
two nodes reflects as closely as possible the
true distance between the corresponding two
pieces in the distance matrix. In Figure 5 we
can see a SplitsTree generated with the
similarity matrix between note histograms. As
expected, due to the different melodic
contour of every style, two clusters appeared:
a first cluster with the deblas, and a second
clearly
distinct
cluster
including
the
martinetes.
Dress, A., Huson, D. and Moulton, V. (1996).
Analysing and visualizing sequence and distance
data
using
SPLITSTREE.
Discrete
Applied
Mathematics, 71:95-109.
Gascuel, O. (1997), “BIONJ: an improved version
of the NJ algorithm based on a simple model of
sequence data,” Molecular Biology and Evolution,
14, pp. 685-695.
Gómez, E. Klapuri, A. Meudic, B. (2003). Melody
Description and Extraction in the Context of Music
Content Processing, Journal of New Music Research
Vol.32 .1, 2003.
Conclusions and future work
Gómez, E. and Bonada, J. (2008). Automatic
Melodic Transcription of Flamenco Singing,
Conference on Interdisciplinary Musicology, 2008,
Thessaloniki, Greece.
Melody is one of the most important aspects
to be considered in the analysis of a cappella
flamenco singing styles. Automatic melodic
description tools can be effectively used to the
analysis of flamenco singing. These automatic
tools
allow
obtaining
quantitative
and
qualitative measures that can complement
historical and musical data (and other types)
on the
roots and
evolution
of
oral
transmission of flamenco styles.
Hernández Jaramillo, J. M. (2002). La Música
Preflamenca, Sevilla, Consejería de Relaciones
Institucionales - Junta de Andalucía.
Haus, G. and Pollastri, E. (2001). An Audio Front
End for Query-by-Humming Systems. In Proc. of
ISMIR 2001, Bloomington, IN, Usa, Oct.
Acknowledgments. This work is partially
funded by Agencia Andaluza para el
Desarrollo del Flamenco, Spain through the
COFLA project: Computational analysis of
flamenco music.
Huson, D. H. (1998). SplitsTree: Analyzing and
visualizing
evolutionary
data,
Bioinformatics,
14:68-73.
Lerman, M., Martens, J. P. , De Baets, B. De
Meyer, H. MAMI. Versión 2.0, 2003. University of
Ghent, http://www.ipem.ugent.be/MAMI.
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