特集 音楽と科学［招待論文：研究論文］

Musical Diversity in India

A Preliminary Computational Study Using Cantometrics

インドにおける音楽多様性

カントメトリクスを用いた計算的予備研究

Hideo Daikoku

Master’s Program, Graduate School of Media and Governance, Keio University
大穀 英雄
慶應義塾大学大学院政策・メディア研究科修士課程

Anna L. C. Wood

President, Association for Cultural Equity, New York City, USA
アンナ ロマックス ウッド
文化的平等協会代表 、アメリカ、ニューヨーク

Patrick E. Savage

Associate Professor, Faculty of Environment and Information Studies, Keio University
パトリック サベジ
慶應義塾大学環境情報学部准教授
Correspondence to: psavage@sfc.keio.ac.jp

Abstract:

The degree of musical diversity within and between societies is a topic oflong-standing debate with theoretical implications for cross-cultural musicalcomparison. Standardized cross-cultural data on musical variation from Alan Lomax’s
Cantometrics Project newly made available via the Global Jukebox allows us toattempt to quantify this variation. Here we perform a case study of traditional Indian
vocal music using the 37 Cantometric features for 207 traditional songs from 32
Indian societies. Principal Component Analysis identifies complexity of vocal styleas the primary factor shaping musical variation in India, consistent with Lomax’s
previous analyses of global variation. We find only very minor musical differences on
average between speakers of different language families (Indo-European, Dravidian,and Austro-Asiatic), but greater variation between societies within language families.
Additionally, we demonstrate how quantitative analysis can be strengthened byobservations from musicians with direct experience of local traditions and applied to
local/regional musicological questions.

34Musical Diversity in India
社会内、また社会間において、どれほどの音楽的多様性が存在するのか。これに関しては複数の文化に及ぶ音楽比較への論理的な考察と共に、長年議論されてきた。Alan Lomax のカントメトリクス研究によって標準化された、多文化にわたる音楽的バリエーションのデータは Global Jukebox により新たに利用可能になり、それによりこのバリエーションを定量的に分析することが可能になった。本研究ではカントメトリクスの 37 の特徴に基づき、インドの伝統的な歌に対するケーススタディを行った。分析対象はインドの 32 地域から選んだ伝統音楽 207 曲である。主成分分析によって求められた、インド音楽のバリエーションを形成する主な要素が、Lomax が世界中の音楽に対する先行分析で求めた結果と一致した。このことから、インドに存在する歌唱法の複雑性が示される。異なる語族の話者（インドヨーロッパ、ドラヴィダ、南アジア語族）の間には、ごく小さな音楽的差異しか見られなかったものの、同じ語族内の複数社会の音楽を比較した際には、より大きなバリエーションが見られたのだ。また、地域的な伝統を直接経験した音楽家と共同研究をすることによって、定量分析はより強固なものになり、さらに地域的な音楽学上の問いに対しても適用可能であることが示された。

Keywords: cross-cultural, diversity, music, India, Cantometrics

異文化、多様性、音楽、インド、カントメトリクス

1Introduction

1.1

Quantifying cross-cultural diversity
The question of how to compare and quantify differences within and between

societies is a challenge in cross-cultural research (Bell et al., 2009; Jacoby et al.,2020; Ross et al., 2013; Rzeszutek et al., 2012). In linguistics, for example, analysesof the evolution of Indo-European languages use one set of vocabulary to represent
English, one to represent French, etc., and reconstruct the evolutionary history ofthese languages by quantifying and modeling the similarities and differences in thesesets of vocabulary (Bouckaert et al., 2012). While this approach yields findings
regarding variation between languages, it circumvents internal variation within
languages (e.g., dialects). In contrast, analyses of variation among dialects within asingle language can uncover important patterns, but these are then difficult togeneralize and compare with other languages (Labov, 1994-2010). Simultaneously
accounting for variation within and between languages is a major challenge and calls
for careful choice of units of analysis and the variables used, and accounting for the
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特集 音楽と科学

specific variables implicated in variance.
This challenge may apply as much or more to other domains such as music,
where internal variation can be substantial enough that, in many cases, a single
example may not be representative of a society for the purpose of cross-cultural
analysis. Alan Lomax’s Cantometrics Project (Lomax, 1968, 1980; Savage, 2018;
Wood, 2018) was a sweeping attempt to account for global musical variation andevolution using quantitative methods to compare music cross-culturally across anumber of variables. It produced a dataset of approximately 6,000 songs that
contained multiple songs from most societies. A criticism of the study was that too
few examples were used to represent and account for internal variation within asociety (cf. Savage, 2018; Wood, 2018). Lomax and his collaborator, Victor Grauer,
maintained that in most societies one main musical style emerges, regardless ofgenre. One of the approaches used in Cantometrics was to derive “modal profiles”
representing the most common musical characteristics for each society and compare
these modal profiles cross-culturally. This treatment has been contested by some (cf.
discussion and references in Savage, 2018), but its validity has yet to becomprehensively tested on a global scale.
Although the issue of how to quantitatively compare societies while accounting
for internal diversity is complex, new statistical methods such as cluster analysis andthe Analysis of Molecular Variance (AMOVA; Excoﬃer et al., 1992; Rzeszutek et al.,2012) allow us to test such assumptions and simultaneously quantify variation within
and between societies. A study of 259 structural aspects of traditional group songs
from twelve indigenous populations in Taiwan and the Philippines showed muchgreater variation within populations than between populations (98% vs. 2%,
respectively; Rzeszutek et al., 2012), paralleling recent findings in genetics and inother domains of culture (Handley & Mathew, 2020). A recent study of global
variation in musical behaviors, as recorded in ethnographic texts, also argued thatmusic varied more within than between societies (approximately 75% vs. 15%,
respectively; Mehr et al., 2019). Importantly, however, neither of these studies
included data on singing style (e.g., vocal tension, vocal blend), which Lomax had

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Musical Diversity in India

argued was the aspect of music that varied least within societies and varied mostbetween societies. Quantification of within vs. between society variation was notpreviously applied to Cantometric data on singing style because the data was notready for public release until now.
The recent digitization and public release of Lomax’s Cantometric data on the
Global Jukebox (Wood et al., In prep.) and D-PLACE (Kirby et al., 2016) allows us
to reanalyze Cantometric data to answer these questions about the structure of crosscultural musical diversity. The Global Jukebox dataset includes standardized
Cantometric classifications originally based on 37 musical features (Table S1; see
Lomax & Grauer 1968 for full details) for approximately 6,000 traditional songs
from approximately 1,000 societies (Fig. 1), in addition to various other cross-culturalexpressive arts datasets and educational tools. Although the system was not designed
to work at that level of analysis without weighting and considerable refinement, somecritics argued that the validity of Cantometrics would depend on its ability to provide
meaningful results at a local or regional scale (e.g., Feld, 1984). The goal of thispaper is to attempt such a local/regional analysis focusing on the question of diversity
within and between societies in India.

Figure 1 A map of the current Global Jukebox sample of recordings of traditional songs
that have been coded using Cantometrics. (from Wood et al., In prep.)

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1.2

Case study: Northern vs. Southern music in India
We chose India as the focus of our study because it allows us to explore multiple

layers of diversity within the scope of a single country. India has both one of thelargest populations in the world and one of the highest levels of cultural diversity,
containing approximately 1/6th of the world’s population and ~1/15th of its languages(Hammarström et al., 2020). It also has a long tradition of scholarly study ranging
from pre-colonial treatises by Indian theorists to recent applications of state-of-the-art
music information retrieval (MIR) technologies (Jairazbhoy, 1995; Rowell, 2015;
Serra, 2017; Vidwans et al., 2019), and is an important emerging market for the musicindustry. Finally, our first author (HD) was born and raised in India with roots in boththe north and the south (New Delhi and Chennai, respectively), allowing us toincorporate personal experience of living in India and playing Indian music as weinterpret our results.
Music pervades all important aspects of Indian society, be they devotional,
festive, or social on a smaller scale. Indian classical music is tied to tradition, folklore
and history, and is intrinsically linked with Indian identity. A wide variety of musicalstyles make up what is known as Indian music. Although Raga and Taal (roughly
analogous to scale and meter in Western music) characterize classical music theory,
Western concepts of polyphony and harmony are essentially absent. Broadly
speaking, one can consider the two categories of Hindustani (North Indian) and
Carnatic (South Indian) as being the major styles that define Indian classical music(Wade, 1979; Binathi, 2012).
These Northern and Southern classical traditions are only one layer of musicalstyle that coexists with and interacts with other styles such as popular and indigenous
folk traditions, which may be shaped by other influences such as social layering,tribal influences, language, and global trade connections of great antiquity. The
degree to which the northern vs. southern distinction is relevant beyond the classicaltraditions has not been previously tested systematically. Such an analysis could shedlight on hitherto unexplored aspects of regional variation within India.
Linguistically, India is extremely diverse. Most Indian languages belong to two38

Musical Diversity in India

broad language families, Indo-European and Dravidian. About 73% of the population
speak languages in the Indo-Aryan branch of Indo-European, while 24% speak
Dravidian languages (Central Intelligence Agency, 2020; Meenakshi, 1995). There
are a total of 450 tribal communities that speak 750 languages from the AustroAsiatic, Dravidian or Tibeto-Burman (Singh, 1992; Kosambi, 1965) families,
comprising roughly 3% of India’s population.
Linguistic groupings are often used as a proxy for cultural groupings, but inmany cases language and culture are not homologous. For example, a study of the
Kol tribal populations suggests that in India, certain groups that have a different
genetic ancestry share a common language as a result of sociocultural pressures toconform (Srivastava et al., 2020). Basu et al. (2003) suggest that the caste systemblurred distinctions in local languages and forced several groups to adopt languages
that favored upward mobility. Centuries of mixing, colonialism and migration havemade these distinctions more and more interconnected. A long-standing debate onwhether Austro-Asiatic or the Dravidian-speaking tribal groups were the original
inhabitants of India (Thapar, 2003) has yielded plausible evidence suggesting AustroAsiatic families predate the Dravidian settlement, and that Dravidians were pushed
further south in order to avoid colonization (Basu, et al., 2003). Distinctions are notwithout classist undertones; upper caste Indians are largely closer to Central Asian
populations. Religion favored the marriage of only certain communities of Indiansfrom specific castes, resulting in a phenomenon where Indo-Europeans andDravidians became major contributors to Indian culture and society.
Since cultural relationships cannot be completely predicted by language, music
may offer an alternative to complement linguistics for predicting human population
history (Lomax, 1980; Brown et al, 2014; Matsumae et al., 2019). Brown et al. (2014)
found significant correlations between musical and genetic diversity amongindigenous Taiwanese populations, suggesting that music and genes may have been
co-evolving for quite some time. Meanwhile, similar studies comparing music,
languages, and genes for a different group of northeast Asian populations found thatgrammar rather than language family classifications was a stronger predictor of
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特集 音楽と科学

population history than music (Matsumae et al., 2019). Overall, it appears that
language, music, and cultural history vary somewhat independently (Feld & Fox,
1994; Pamjav et al., 2012; Brown et al., 2014; Matsumae et al., 2019), suggesting thatcare should be taken constructing analyses of music and its relationships with socialgroups and linguistic and cultural history.
Our goal is to look at the within vs. between culture variation in Indian music inorder to get a broader understanding of Indian musical diversity and cultural history.
Because language is often used as a proxy for cultural relationships, we will focus on
a comparison between Indo-Aryan-speaking societies from northern India vs.
Dravidian-speaking societies from southern India, excluding groups officiallydesignated as “tribal”. But because language and oﬃcial designations are imperfectcultural proxies, we will also include an analysis including all societies in India with
Cantometric data (i.e., including the Portuguese-speaking Goa people, “tribal”, and/orAustro-Asiatic-speaking societies) to explore the extent to which musical patterns
map onto linguistic ones (see map in Fig. 2 for details on the societies included, theirlanguage classifications, and musical sample sizes).
We will use the Cantometrics dataset to explore diversity within and between
north and south Indian music based on 37 Cantometrics features (cf. Table S1 for a
full list of features). Section 2 of the paper will look at a Principal Component
Analysis (PCA) to visualize a representation of the dataset. We then perform an
Analysis of Molecular Variance (AMOVA) to look at the degree of variance within
and between societies. In section 3 we discuss our results and conclude withlimitations and future directions for this research.

2

Methods

2.1

Musical sample
The standardized Cantometric classification data in the Global Jukebox allows

us to quantitatively explore both similarities and differences in the repertoires ofNorthern and Southern India. The Cantometrics dataset contains standardized
classifications for 37 musical features for 207 recordings from 33 societies throughout

40Musical Diversity in India

India, including both classical (Hindustani and Carnatic) and folk traditions. While amajority of these recordings are classified as tribal (150 songs), we focus our analysis
on the 57 recordings classified as ʻnon-tribal’ (Ishtiaq, 1999; Central Intelligence
Agency, 2020; Meenakshi, 1995). Of these, we focus on societies speaking Indo-

Figure 2 A map of the Cantometric sample in India. Circle size indicates the number ofsongs, with 208 songs from 32 Indian societies, with a total of 101 songs from
Dravidian-speaking cultures, 55 from Indo-Aryan-speaking cultures, 51 from
Austro-Asiatic-speaking cultures, and 1 from Portuguese-speaking Goa(Portuguese belongs to the Romance branch of the Indo-European languages
and is distantly related to the Indo-Aryan branch). Because the Goan sample
was so small and its history so different from the rest of the sample, it was notincluded in the analyses.
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特集 音楽と科学

Aryan or Dravidian languages, with 28 and 19 recordings respectively. We omit fromthis subset the 10 recordings that are from Austro-Asiatic languages, as they trace anentirely different cultural lineage and comprise less than 3% of the total population(Honokla et al., 2018).
2.2

Principal Component Analysis (PCA)
To analyze and visualize musical diversity within the Indian sample ofCantometric data, we performed a Principal Component Analysis (PCA; Fig. 3). PCA
condenses multidimensional data into a lower dimensional space by maximizing
variance in each component (Wold et al., 1987), and is commonly used for
multivariate analysis of cultural data, including music (Turchin et al., 2018; Mehr etal., 2019). We find that both the first and second principal components (PCs) each

Figure 3 S cree plots of the various Principal Component Analyses (PCA) we conductedon our dataset. The full dataset consists of all 207 recordings (excluding
Portuguese Goa), and all features means the PCA was calculated with all 37
Cantometrics features. A partial dataset denotes 47 (Indo-Aryan & Dravidian
non-tribal) recordings, and partial features is PCA calculated with 26 features(excluding 11 quasi-redundant features). From these figures we can see theproportion of variance explained by eight principal components for all four
PCs. See Table 1 for the variable loadings.

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Musical Diversity in India

explain ~17% of variance in the 37 features. After this the proportion of varianceexplained suddenly drops to below 8% (see Fig. 3 for the proportion of varianceexplained by other dimensions). Upon re-conducting our analysis to include only
Indo-Aryan & Dravidian non-tribal cultures, we find that the first PC explains ~23%of the variance and the second PC explains ~11%.
Importantly, the songs do not display any language-family or region based
clustering. Instead, Indo-Aryan, Dravidian and Austro-Asiatic songs are dispersed
widely across the two-dimensional space of the PCA (Fig. 4), suggesting that
language family distinctions do not correlate with musical diversity within India.

Figure 4 F ull PCA plot of all 207 tracks measured across 37 features. We interpret thefirst two principal components as vocal complexity and instrumental complexity
respectively. A, B, C, D and E are selected tracks discussed in greater detail in
Fig 6.
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We interpret Dimension 1 as being best described as capturing “vocal
complexity”. This is because dimension 1 is most strongly correlated with acollection of features spanning the diverse categories of musical organization(rhythmic coordination of the vocal group, tonal blend of the vocal group), vocal
texture (musical organization of the vocal part, and tremolo), social organization(social organization of the vocal group), and articulation (repetition of text; see Table
1 for PC variable loadings). These all tend to covary such that solo singing tends tohave more complex ornamentation and structure (appearing toward the right-handside of Fig. 4), while group singing tends to be more repetitive and less ornamented(left-hand side of Fig. 4), consistent with correlations among features previously
identified by Lomax (1980). We will use the term “vocal complexity” to describe
PC1.
In contrast, PC2 does not correspond to a diverse set of categories; instead all of
the features with the highest loadings are instrumental features (rhythmic relationshipwithin the orchestra, rhythmic coordination of the orchestra, musical organization of
the orchestra, social organization of the orchestra, and tonal blend of the orchestra).
Songs appearing closer to the bottom of Fig. 4 tend to have larger, more coordinatedinstrumental accompaniment, while those closer to the top tend to be unaccompanied
songs. We will use the term “instrumental complexity” to describe PC2.
The full sample analyzed in Fig. 4 is not entirely representative of Indian music
demographically. The 2:5 ratio of non-tribal vs tribal recordings and the large numberof Austro-Asiatic tracks in our sample are not proportional to actual population
statistics in India, but the sample does capture an unusually diverse and complex setof cultural and historical relationships (Central Intelligence Agency, 2020). To
investigate the impact of this sample, we redid the analysis on a smaller sub-sampleof the Indian recordings after excluding tribal and Austro-Asiatic-speaking societies
to focus more on the historical distinction between Indo-Aryan speaking societies innorth India and Dravidian-speaking societies in south India.

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Musical Diversity in India

Figure 5 T he first two components from a PCA of the subset of 47 Indian songs from
non-tribal Indo-Aryan and Dravidian-speaking populations in the Cantometrics
sample. Individual points are shaded by language (Indo-Aryan vs. Dravidian)and labeled by society. Refer to Fig. 6 for a detailed description of each of theselected tracks A, B, C D, and E.

Fig. 5 shows a revised PCA on a subset of the full sample. Here we exclude
Austro-Asiatic and tribal societies. The 47-song sample also fits into similar principal
components. PC1 corresponds with musical organization, ornament and socialorganization variables (rhythmic coordination of the vocal group, repetition of text,
tremolo, and social organization of the vocal group). PC2 corresponds to theorchestral features: the musical organization of the orchestra, overall rhythm of theorchestra and social organization of the orchestra (Table 1).
Cantometric codings for five selected recordings from the dataset exemplify
variation in style and form in this sample (Fig. 6). Example A, a devotional song from
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Table 1 V ariable loadings for the PCA plot shown in Fig. 4 (Full PC1& Full PC2) and
Fig.5 (Subset PC1& Subset PC2).
Line

Title

Subset PC2

Full PC1

Full PC2

1

THE SOCIAL ORGANIZATION OF THE VOCAL
-0.24
GROUP

Subset PC1

0.1

-0.24

0.14

2

RELATIONSHIP OF ORCHESTRA TO VOCAL
-0.02
PARTS

-0.22

0.07

0.25

3

SOCIAL ORGANIZATION OF THE ORCHESTRA -0.2

-0.3

-0.07

0.34

4

MUSICAL ORGANIZATION OF THE VOCAL
-0.23
PART

0.16

-0.26

0.02

5

TONAL BLEND OF THE VOCAL GROUP

-0.23

0.16

-0.3

0.07

6

RHYTHMIC COORDINATION OF THE VOCAL
-0.27
GROUP

0.11

-0.31

0.11

7

M U S I C A L O R G A N I Z AT I O N O F T H E-0.14
ORCHESTRA

-0.38

0.0

0.36

8

TONAL BLEND OF THE ORCHESTRA

-0.11

-0.2

-0.08

0.32

9

R H Y T H M I C C O O R D I N AT I O N O F T H E-0.19
ORCHESTRA

-0.28

-0.09

0.34

10

REPETITION OF TEXT

-0.24

-0.09

-0.27

-0.02

11

OVERALL RHYTHM: VOCAL

0.15

-0.01

0.1

-0.13

12

RHYTHMIC RELATIONSHIP WITHIN THE-0.21
VOCAL GROUP

0.1

-0.24

0.06

13

OVERALL RHYTHM: ORCHESTRA

0.05

-0.33

0.09

0.29

14

RHYTHMIC RELATIONSHIP WITHIN THE-0.22
ORCHESTRA

-0.3

-0.09

0.34

15

MELODIC SHAPE

-0.09

-0.05

-0.06

-0.06

16

MELODIC FORM

-0.19

0.08

-0.23

-0.14

17

PHRASE LENGTH

-0.15

-0.02

-0.08

-0.09

18

NUMBER OF PHRASES

-0.18

0.02

-0.21

-0.12

19

POSITION OF FINAL TONE

0.11

-0.06

0.06

-0.03

20

MELODIC RANGE

0.06

-0.14

0.06

0.02

21

INTERVAL SIZE

-0.16

0.19

-0.21

-0.09

22

POLYPHONIC TYPE

-0.08

0.17

-0.14

-0.03

23

EMBELLISHMENT

-0.15

0.23

-0.23

-0.14

24

TEMPO

-0.16

-0.04

-0.14

0.03

25

VOLUME

-0.08

-0.27

-0.09

0.14

26

RUBATO: VOCAL

-0.23

0.01

-0.22

0.07

27

RUBATO: ORCHESTRA

-0.17

-0.04

-0.05

0.0

28

GLISSANDO

-0.2

0.05

-0.11

-0.03

29

MELISMA

-0.13

0.15

-0.16

-0.08

30

TREMOLO

-0.24

-0.09

-0.27

-0.01

31

GLOTTAL

-0.15

0.17

-0.12

-0.11

32

VOCAL PITCH (REGISTER)

-0.1

0.02

-0.06

-0.1

33

VOCAL WIDTH

-0.11

0.08

-0.12

-0.2

34

NASALITY

-0.09

0.05

-0.16

-0.1

35

RASP

-0.02

0.03

-0.06

-0.09

36

ACCENT

-0.1

0.02

0.04

-0.1

37

ENUNCIATION

-0.13

-0.1

-0.16

-0.08

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Musical Diversity in India

Kerala, is a raga praising Hindu deities. The track relies mostly on the tanpura as a
backdrop for the undulating male vocals in the alaap section. After that, the ragapicks up, in both structural and orchestral complexity; we hear a tabla driving themeter of the song. This track is perhaps most typical of the structure of a classicalCarnatic-Dravidian song.
Example B, a Bhajan from Uttar Pradesh, North India, is a devotional songor bhajan. It has a fairly more complex orchestra with more instruments thanFigure 6 A
 , B, C, D, and E are screenshots from the Global Jukebox showing full codings
for the 37 Cantometric features for each recording1).

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example A. The vocals consist of a group-leader overlap in rhythmic unison.
Example C is also a song from Kerala and is written in praise of the god
Ganapati. This track has no orchestra. The absence of an orchestra is perhaps why it
is so distanced from example A in the PCA plot in Fig. 5. The song has an alternatingstyle between the leader and group in irregular meter. While the singing isembellished, there is little tremolo, melisma and glottal shake. While more vocallycomplex than track B, it has relatively simple vocals when compared to example D.
Example D, Viraha from Benaras, Uttar Pradesh, is a milking song with tragic
overtones, sung with a Nagara drum as accompaniment. Two women alternate
singing embellished descending melodic lines in slow tempo. The simple strophe,
long phrases, asymmetrical phrases and extreme rubato, sung in a high, tense, nasal,
raspy voice give it great complexity. Its vocal style is more elaborate than example
B, despite being more or less on the same end of the spectrum in terms of orchestralcomplexity.
Example E is another bhajan from Benaras, Uttar Pradesh. The song has simple,
short phrases with a slightly high melodic range and little ornamentation. The register
is narrow, and the vocals are quite relaxed. It is similar to track C in its lack of anorchestra, irregular meter, undulating melody, and other orchestral dependantfeatures. Many of these features are quasi-redundant, which may be a reason why
tracks E and C are close to each other on the plot, but are musically quite different.
The distinction becomes more relevant in our revised PCA in section 2.2.1 (Fig. 7).
2.2.1

PCA re-analysis

Several Cantometric features have quasi-redundancies built in, which may affectthe correlation structure found in the PCA. For example, when a song is coded ashaving no instrumental accompaniment for Line 7 (musical organization of theorchestra, cf. Table 1), it must necessarily also be coded as having no instrumental
accompaniment for Line 8 (tonal blend of the orchestra). Similarly, redundancies
apply for songs coded as solo singing, such as for Line 4 (musical organization of the
vocal part) and Line 5 (tonal blend of the vocal group).

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To determine whether this affected our PCA, we repeated the PCA using only asubset of 26 features after excluding the 11 quasi-redundant features (Fig. 7; cf. Table
S1 for full list of quasi-redundant features).

Figure 7 W
 e repeat the PCA in Fig. 4 using only a subset of 26 features after excluding 11
quasi-redundant features (cf. Table S1 for a list of specific features included/
excluded).

As in Fig. 4, the revised PCA in Fig. 7 shows no sign of any Indo-Aryan vs.
Dravidian clustering. PC1 explained ~20% of the variance and PC2 explained ~11%.
Analysis of the variable loadings (Table S2) suggests that PC1 remains interpretableas “vocal complexity”, but the second component now appears to capture “dynamics”
rather than “orchestral complexity”. Specifically, many of the same features as in theprevious analysis (repetition of text, tremolo, melodic form, number of phrases, andvocal rubato) still have the highest loadings with PC1, however they are less
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characterized by vocal features and more by overall musical structure. We can seethat recordings A and B are quite similar in terms of musical structure, and hencealign closely along PC1. It may be a coincidence that these are both devotional/
religious songs, but it would be worth investigating to see if devotional songs follow
some common patterns in musical structure. The one instrumental feature retained(musical organization of the orchestra) also maintains a high loading on PC2, but
now includes a number of other features related to dynamics (vocal pitch, melodic
range, accent and overall rhythm and tempo). Thus, in the revised analysis, PC2 may
be better interpreted as “dynamics”. Recording E shows a high degree of variation inthis regard, as it is fast, loud and has a melodic range of over two octaves. In contrast,
recordings B, C, D and E are relatively simple.
2.3

Within and between society diversity
Analysis of Molecular Variance (AMOVA) (Excoﬃer et al., 1992) is a techniquefor quantifying variation within and between populations. It was originally designed
for population genetics, but has been adapted to cultural domains including music
and folklore (Rzeszutek et al., 2012; Ross et al., 2013) by treating specificcombinations of cultural features as analogous to a genetic haplotype (specificcombination of genetic variants). Here the populations are the societies in our sample.
The AMOVA framework can accommodate multiple levels of structure - here we
conducted our analysis using units of song (lowest level), society (next level, e.g.,ethnolinguistically defined cultural group such as Kullu, Kerala or Gond 2)), andlanguage family (highest level).
We used R’s ade4 package to produce the analysis below (Thioulouse et al.,2007). The matrix of distances between songs was calculated by taking the euclidean
distance between all features of recordings. For this analysis we excluded 8 societies
represented by only a single song (Central Indian Folk, Santhal Munda, Saurashtra,
Bhil, Banjara, Andh, Jhodia and Portuguese Goa) because they would not allow us toquantify within-society diversity.
The results of the AMOVA analysis are shown in Table 2A below. The degree of50

Musical Diversity in India

variation within societies was approximately three times greater than the degree ofvariation between societies, and only a very minor average difference between IndoAryan and Dravidian regions was observed. Only ~1% of all variation among songsin the sample could be explained by differences between language families, while~25% of variation could be explained by differences among societies within language
families). Interestingly, while the proportion of variation between families remains~1% when analyzing only the subsample of non-tribal, non-Austro-Asiatic-speaking
societies, the proportion of variation between societies within families for this subset
analysis is approximately five times smaller than it is for the full sample (Table 2B).
When this analysis was repeated using only the 26 independent Cantometrics, theresulting amount of between-society variation was intermediate between the othertwo analysis methods (~11%; Table 2C).
Table 2 2 A is an AMOVA of N=200 recordings from 3 Indian language families and 25
societies after removing societies where there is only one sample per society. 2B isan AMOVA of N=47 recordings from 2 Indian language families and 9 societies.
Here we also remove societies where only one sample is present. 2C is the same
analysis N=47 recordings from 9 societies on 26 features (See Fig. 7 for PCA).
2A. Analysis of Molecular Variance (AMOVA) for Cantometrics in India
Results

Component of Covariance
Degrees of Sum Sq.
freedom

Mean Sq.

Sigma%

Between language
families

2

20.76

10.38

Variation
between language
families

0.032

1.33

Between societies
within language
families

22

141.91

6.45

Variation
between societies
within language
families

0.61

25.21

Within societies

175

312.38

1.79

Variation withinsocieties

1.79

73.46

Total (N-1)

199

475.05

2.39

Total variation

2.43

100

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特集 音楽と科学

2B. AMOVA for non-tribal Dravidian/Indo-Aryan Cantometrics in subsample India
Results

Component of Covariance
Degrees of Sum Sq.
freedom

Mean Sq.

Sigma%

Between language
families

1

4.42

4.43

Variation
between language
families

0.015

0.523

Between societies
within language
families

7

22.70

3.24

Variation
between societies
within language
families

0.148

5.271

Within societies

38

100.54

2.64

Variation withinsocieties

2.646

94.21

Total (N-1)

46

127.67

2.77

Total variation

2.808

100

2C. AMOVA for non-tribal Dravidian/Indo-Aryan Cantometrics in subsample India with a subset of features
Results

Component of Covariance
Degrees of Sum Sq.
freedom

Mean Sq.

Sigma%

Between language
families

1

2.35

2.36

Variation
between language
families-0.03-2.107

Between societies
within language
families

7

15

2.14

Variation
between societies
within language
families

0.17

10.64

Within societies

38

55.38

1.46

Variation withinsocieties

1.46

91.46

Total (N-1)

46

72.75

1.58

Total variation

1.59

100

3

Discussion

3.1

Correlated variation among Cantometric features
Our PCA analyses showed that many Cantometric features captured correlated

aspects of musical variation. The strongest covariation appeared to correspond to thestructural complexity of the vocal part, ranging from complexly ornamented solo
singing to simple, repetitive group singing. This component consistently appeared as
the first PC in all three analyses (full sample, sub-sample, and subset of variables),consistent with Lomax’s claim that many aspects of vocal style tend to consistently
covary throughout the world (Lomax, 1980). The second strongest component
appeared to be explained by instrumentation and/or dynamics, but this second PC

52

Musical Diversity in India

was more variable in the three different analyses, suggesting it may be more sensitive
to the particular makeup of the sample and analysis methods.
3.2

Variation within and between societies
Our analysis of musical variation showed that variation within societies was

greater than variation between societies, and that average differences between
language families in our Indian sample were negligible (~1%). This is consistent
with previous studies finding greater musical variation within societies than between
them (Rzeszutek et al., 2012; Mehr et al., 2019). However, the degree of variation
within societies varied substantially depending on the precise nature of the sample,
with only ~5% of variation between societies for the sub-sample of non-tribal, nonAustro-Asiatic-speaking societies vs. ~25% for the full sample and ~11% for analternative analysis using only the 26 Cantometric features that are not partiallydependent on other features. This suggests that the relative degrees of within vs.
between society diversity can be strongly affected by the nature of the sample andanalysis methods, and thus any general conclusions about within vs. between society
diversity may be premature until we can analyse a full global sample.
3.3

Limitations of this study
This study aimed to explore large-scale questions using a restricted set of

Cantometric data from a single nation, so all general conclusions should be treated as
preliminary.
Even within India, the Cantometric sample is not representative of the Indian
populace in general. There is a high proportion of tribal music, and almost all songs
are folk songs. The linguistic distribution of peoples in our musical sample is notproportional to the demographic proportion of people living in India. One way toaddress this is to add new samples that balance out the number of tribal cultures with
non-tribal cultures, and also introduce modern folk and classical music.
Analogies with genetics and language also come with caveats. While suchanalogies have been helpful for developing theories and methods to address crossKEIO SFC JOURNAL Vol.20 No.2 202053

特集 音楽と科学

cultural musical diversity and change, it is important to remember that each domain
has its own unique mechanisms and dynamics that may not necessarily apply to theother domains (Savage, 2019). In particular, while this analysis has adapted methodsof quantifying diversity originally developed for population genetics and later
adapted to music and other cultural domains, the assumptions of these methods have
not yet been rigorously tested for music. Future studies should explore these more
rigorously, including exploring the degrees to which different musical features mayvary more or less within and between societies. Other areas worth exploring include
using more fine-grained analysis of linguistic relationships beyond simply language
family classifications, and also other types of cultural classifications that do not rely
only on language.
It is important to emphasize that this is an analysis of Cantometric ratings, notthe audio sample of the music itself. Future analyses should apply automatic MIR
methods such as feature extraction and machine learning in tandem with these
Cantometrics ratings to investigate how the audio recordings of the music compareswith Cantometrics codings and use the Cantometric codings as training data todevelop machine learning methods that can be applied to non-Western musicalexamples (cf. Panteli et al., 2017; Panteli et al., 2018; Daikoku et al., 2020).
Lomax noted that Cantometric variables would need to be refined and, in somecases omitted, for intra-area analyses. This approach could be used to investigate
what features of music link cultures and areas together. Looking at the nature ofmusical variation within cultures, one may find that here are similar patterns ofvariation within songs of the same area. Cantometrics has uncovered many of thecommon characteristics of folk songs in each region and the musicological threadthat ties regions together. Future work would entail broadening and refining suchconnections, for instance, by exploring how perceptual ratings from expert
Cantometrics coders can weigh certain acoustic properties over others in determining
how styles are linked with each other.

54

Musical Diversity in India

3.4

Implications for Indian music
The fact that our analysis found only minor differences between northern vs.

southern Indian music as a whole, but substantial differences between different
societies within these regions, suggests that while the division of Indian classicalmusic into northern (Hindustani) and southern (Carnatic) music may be useful for
dealing with nuances of the classical traditions, a north-south distinction is anoversimplification that is not useful when considering variation in traditional music
throughout India as a whole.
Having grown up in New Delhi, our first author was primarily exposed to North Indianclassical music, but his relationship with music was primarily functional. Different songs weresung for formal occasions, weddings and performances, but were collectively deemed
ʻHindustani’ music. When he visited his mother’s extended family in Kerala and Chennai, he
could instantly tell there was a difference in the style of Carnatic music, but in retrospect foundthose differences to be driven by language and context, more so than style and musicality.
Religion was perhaps the main unifying factor in all of it, a thread that tied different styles
together by a unifying mythos of the Ramayana and Mahabharata. He attended a few guest
workshops by regional artists, mostly from Rajasthan and Orissa. He heard styles andinstruments he had never heard of before. He was taught that music was primarily ceremonial,and styles would differ greatly depending on one’s guru. The degrees of variation we find in
our results make logical sense if there is that much diversity in a single society of music.
The question of within vs. between culture variation is also important from anempirical point of view, as all classification algorithms attempt at learning
embeddings where inter-cultural distance is maximum and intra-cultural distance isminimum. Supervised metric learning algorithms such as LMNN or semi-supervisedalgorithms such as ITML (Weinberger et al, 2009) are predicated on optimizing
within vs. between group variation. Modeling variation in expert-annotated
Cantometrics data may help develop analytical tools to help musicologists annotate
and classify music at scale. In that respect, an expanded global analysis of within vs.
between culture variation would be crucial, ideally including both traditional andcontemporary music.
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特集 音楽と科学

By applying statistical methods to a case study of Cantometric data from India,
we have shown how it is possible to quantify and compare variation within andbetween societies to inform our understanding of musical diversity and apply it topractical tools for computational and comparative musicology. Now that once
Cantometric data from throughout the globe will be publicly available, it will bepossible to build upon Lomax’s original work with these. One can then expand thesetypes of analyses to further our understanding of cross-cultural musical diversity andits implications for society. Although this case study of diversity within traditional
songs from a single nation cannot make strong conclusions regarding global patternsof musical diversity, it highlights ways in which future research can be usefully
expanded to global scales.

Data/code availability
Code and data can be found at https://github.com/comp-music-lab/sfcjournal-2020. This private repository will be made public when Wood et al. (In prep.)
release the full Global Jukebox data (estimated for 2021).
Author contributions
Conceptualization/ Methodology: PES, HD, ALCW; Analysis/ Investigation/ Visualization:
HD, PES; Project administration/ Supervision/ Funding acquisition: PES; Writing – originaldraft: HD; Writing – review & editing: PES, ALCW.

Acknowledgements
We thank S. Fujii for the invitation to contribute to this special issue on Music and Science.
We thank E. Murata, T. Shimozono, T. Mori, J. Murata, Y. Sakurai, and T. Yamashita from
Yamaha for discussion and feedback. We thank Stella Silbert, John Szinger, Kate Kirby, Victor
Grauer, and the Association for Cultural Equity for digitizing and preparing the Global Jukebox
research materials.
HD is supported by funding from the Yamaha corporation. PES is supported by a Grant-inAid from the Japan Society for the Promotion of Science (#19KK0064) and by grants from Keio
University (Keio Global Research Institute and Keio Gijuku Academic Development Fund).

56

Musical Diversity in India

Supplementary material
Table S1 Features categories
Line
Variable name
NumberCategory

Redundancy

1

THE SOCIAL ORGANIZATION OF THE
VOCAL GROUP

Vocal texture

Quasi-redundant(with 4)

2

RELATIONSHIP OF ORCHESTRA TOVOCAL PARTS

Instrumentation

Quasi-redundant(with 7)

3S O C I A L O R G A N I Z AT I O N O F T H E
ORCHESTRA

Instrumentation

Quasi-redundant(with 7)

4

MUSICAL ORGANIZATION OF THE
VOCAL PART

Vocal texture

Independent

5

TONAL BLEND OF THE VOCAL GROUP

Vocal blend

Quasi-redundant(with 4)6

RHYTHMIC COORDINATION OF THE
VOCAL GROUP

Vocal blend

Quasi-redundant(with 4)

7

MUSICAL ORGANIZATION OF THE
ORCHESTRA

Instrumentation

Independent

8

TONAL BLEND OF THE ORCHESTRA

Instrumentation

Quasi-redundant(with 7)9RHYTHMIC COORDINATION OF THE
ORCHESTRA

Instrumentation

Quasi-redundant(with 7)

10

REPETITION OF TEXT

Articulation

Independent

11

OVERALL RHYTHM: VOCAL

RhythmIndependent

12

RHYTHMIC RELATIONSHIP WITHIN
THE VOCAL GROUP

Vocal texture

Independent

13

OVERALL RHYTHM: ORCHESTRA

Instrumentation

Quasi-redundant(with 7)14

RHYTHMIC RELATIONSHIP WITHINTHE ORCHESTRA

Instrumentation

Quasi-redundant(with 7)

15

MELODIC SHAPE

MelodyIndependent

16

MELODIC FORM

Form

Independent

17

PHRASE LENGTH

Form

Independent

18

NUMBER OF PHRASES

Form

Independent

19

POSITION OF FINAL TONE

MelodyIndependent

20

MELODIC RANGEMelodyIndependent

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特集 音楽と科学
21

INTERVAL SIZE

MelodyIndependent

22

POLYPHONIC TYPE

Vocal texture

Quasi-redundant(with 4)23

EMBELLISHMENT

Ornamentation

Independent

24

TEMPO

RhythmIndependent

25

VOLUME

Dynamics

Independent

26

RUBATO: VOCAL

RhythmIndependent

27

RUBATO: ORCHESTRA

Instrumentation

Quasi-redundant(with 7)28

GLISSANDO

Ornamentation

Independent

29

MELISMA

Ornamentation

Independent

30

TREMOLO

Ornamentation

Independent

31

GLOTTAL

Ornamentation

Independent

32

VOCAL PITCH (REGISTER)

Dynamics

Independent
Independent

33

VOCAL WIDTH

Vocal tension

34

NASALITY

Vocal tension

Independent

35

RASP

Vocal tension

Independent

36

ACCENT

Dynamics

Independent

37

ENUNCIATION

Articulation

Independent

Table S2 Variable Loadings of revised PCA (Fig. 7)
Variable

PC1

PC2

OVERALL RHYTHM: VOCAL

0.12

-0.32

POSITION OF FINAL TONE

0.08

-0.26

MELODIC RANGE

0.03

-0.42

VOLUME

-0.05

0.04
-0.31

RASP

-0.05

MUSICAL ORGANIZATION OF THE ORCHESTRA

-0.09

0.09

MELODIC SHAPE

-0.11

-0.16

MELISMA

-0.14

0.13

TEMPO

-0.16

0.31

VOCAL PITCH (REGISTER)

-0.16

-0.35

GLOTTAL

-0.18

-0.05

58

Musical Diversity in India
NASALITY

-0.18

-0.21
-0.05

ENUNCIATION-0.18

MUSICAL ORGANIZATION OF THE VOCAL PART

-0.19

0.020

VOCAL WIDTH

-0.19

-0.27

ACCENT

-0.19

-0.31

INTERVAL SIZE

-0.24

-0.05

EMBELLISHMENT

-0.24

0.05

PHRASE LENGTH

-0.25

-0.03

NUMBER OF PHRASES

-0.25

0.13

RUBATO: VOCAL

-0.27

0.19

GLISSANDO

-0.28

-0.07

MELODIC FORM

-0.30

0.06

REPETITION OF TEXT

-0.31

0.04

TREMOLO

-0.31

0.04

Endnote1)

2)

Audio recordings available for A, B, C, D and E respectively under the following links:
A: https://theglobaljukebox.org/#?share=songDetails_Kerala_Thukkiya%20Thiruvadi
B: https://theglobaljukebox.org/#?share=songDetails_Uttar-Pradesh_Bhajan
C: https://theglobaljukebox.org/#?share=songDetails_Kerala_Kuravarkali:%20
Ganapati%20Stotram
D: https://theglobaljukebox.org/#?share=songDetails_Benares_Viraha
E: https://theglobaljukebox.org/#?share=songDetails_Benares_Saya,%20Folk-Bhajan
Note that while Kullu is a city, Kerala is a state and the Gond are a people. The term“society”is equivalent to ʻculture’ as it was used in Cantometrics, in which musical samples
represented cultures, which in turn were matched to societies described by George P.
Murdock in his Ethnographic Atlas.

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