Thursday
Dec122013

                                                                                                            Michael Lindsey

Cross-Cultural Perceptions of Musical Emotion: Musical Binaries in the Seasonal Ragas of Hindustani Music

 

            Numerous studies have been conducted that test the relationship between music and emotional evocation in listeners.  The studies conducted by Haack, Kratus, and Behrens & Green illustrate the ability and tendencies of listeners in associating musical performance to various emotional conditions.[1]  However, the factors governing the correlation of these emotional cues derived from the listening experience remain under debate.

            As discussed within the empirical study conducted by Balkwill & Thompson, theorists and researchers have suggested that the enculturation of musical cues plays a key role in establishing feelings of emotional affect in listeners.  However, case studies testing the emotional evocation of music outside of the Western art music paradigm have been limited.  Apart from the empirical studies conducted by Balkwill & Thompson, Hoshino, and Deva & Virmani, investigations into the emotional affect of music have primarily focused within the tonal framework of Western art music.  While it is highly probable that musical enculturation governs much of the music listening experience, Balkwill & Thompson’s study suggests that musical universals may exist, which govern musical perception.  The results from their testing of psychophysical elements – speed, loudness, and timbre – in Hindustani music illustrate the ability of listeners to detect emotional cues even within an unfamiliar musical language.  Additionally, the studies by Ali & Peynircioglu, Kessler, Hansen, & Shepard, and Castellano, Bharucha, & Krumhansl show the ability of listeners to assess new musical languages and pick up on intended emotional cues.[2]   

            In the absence of emotional cues associated with familiarity in music, the studies above show that listeners rely on cues established by their own culture’s music (such as tonality in Western listeners) as well as the perception of other, more basic psychophysical cues (such as rhythm and tempo).  As evinced by the results of Balkwill & Thompson’s study, certain psychophysical dimensions within Hindustani music – timbre and musical complexity in particular – have a significant impact on producing high rates of success amongst listeners in perceiving emotions in music.  However, that is not to say that other conventions do not factor into listeners’ perceptions and answers.  A closer look at the melodic framework of the ragas tested by Balkwill & Thompson reveals a correspondence between the melodic framework of ragas tested and conventions within Western tonality.  Ragas associated with feelings of anger, sadness, peace, and joy maintain comparable melodic frameworks to those in Western tonal music that evoke similar emotions.  This being so, one can expect that listeners unfamiliar with Hindustani music will be able to perceive senses of antecedence and consequence – a prominent relationship within the framework of Western tonality – within the melodic frameworks of Hindustani music.

            The present study tests the ability of listeners to perceive emotions in Hindustani music and then to use their perceptions to create a binary relationship between two musical examples.  My hypothesis was that listeners unfamiliar to Hindustani music would be able to perceive intended emotions in musical examples.  Additionally, I hypothesized that listeners would be able to identify and construct an antecedent–consequent relationship between two musical examples if given a vocabulary from which to choose paired descriptive words describing the performed musical examples.

 

The Binary Relationship of Seasonal Ragas in Hindustani Music

            Within the melodic frameworks (raga) of Hindustani music are associations to particular moods and emotional aesthetics (rasa).  A successful performance of a raga is dependent largely upon the ability of an artist to evoke the particular rasa or rasas that correspond to the respective raga.  In addition to being correlated to particular emotions, the performance practice of certain ragas is also related to emotional aesthetics tied to the primary seasons experienced within the geographic realm of Hindustani music – namely summer, monsoon, winter, and spring.  Based on the particular emotional aesthetics associated with these seasonal ragas, opposing binaries arise that form an antecedent-consequent relationship between particular raga pairings.  These binaries exist between summer and monsoon ragas, and between winter and spring ragas.

            This experiment focuses on the binary relationships that occur between the seasonal ragas of Hindustani music.  These relationships exist between the summer raga, Deepak, with two monsoon ragas, Miyan ki Malhar and Megh, and between two winter ragas, Shree and Malkauns, with two spring ragas, Basant and Hindol.  In each instance, the pairs of ragas form a binary relationship with regards to the inherent melodic structures and performance practice relevant to each raga.  The emotional aesthetics attached to the summer raga Deepak are feelings of the relentlessness and unforgiving nature of the extreme conditions associated with the summer season in India.  Raga Deepak’s consequent ragas, Miyan ki Malhar and Megh, are contrastingly characterized by feelings of the abatement and calming aura that comes with the arrival of the torrential monsoon rains, which break the summer heat.  With regards to the winter ragas, Malkauns and Shree, the emotional character of their performance practice centers on feelings of austerity, solemnity, and stagnation.  Their consequent ragas, the spring ragas Hindol and Basant, are contrastingly characterized by feelings of rebirth and the renewal of life and vitality that occurs during the unfolding of the spring season.  While some characteristics of ragas corresponding to similar seasons overlap each other, there are other traits attributed to the seasonal ragas that distinguish the performance practice and emotional aesthetic of each individual raga.[3]  

            Additional moods and emotional aesthetics for the seasonal ragas are obtained from the personification of ragas as detailed by ancient theoretical treatises.  In Hindustani music, ragas are often affiliated with a deity or other prominent historical or mythological figures in Indian culture.  The rasas of these ragas parallel the actions and personalities of these figures, giving them a more robust and diversified individuality.[4]  Table 1 illustrates the antecedent-consequent relationships of the seasonal ragas of Hindustani music.

Table 1

 

Antecedent

Consequent

 

Raga

Deepak

Miyan ki Malhar

Megh

 

Season

Summer

Monsoon

Character

Unrelenting

Harsh

Severe

Abating

Soothing

Calming

 

Raga

Shree

Malkauns

 

Hindol

Basant

Season

Winter

Spring

Character

Austere

Solemn

Stoic

Lively

Vibrant

Spirited

 

 

 

 

 

Method

Listeners

            For this experiment fifteen listeners were tested: one faculty member, four graduate students, and ten undergraduate students from the University of California, Santa Cruz.[5]  Participants in the experiment were not selected based on their familiarity with Western music and only one participant (the faculty member) expressed having a prior familiarity with the Hindustani musical tradition.  Undergraduate participants were given monetary compensation for their partaking in the study. 

Materials

            The listeners of the study were played twenty-eight musical fragments, which were taken from twenty recordings performed by thirteen different artists.  These musical fragments included arrhythmic (taken from the alap portion of the performance) as well as rhythmic (taken from the gat portion of the performance) examples.[6]  A variety of performance genres were tested, which included vocal, aerophone (bansuri), and plucked, struck, and bowed chordophone (sarod and sitar, santoor, and sarangi respectively) musical examples.  The performers of the musical examples are all considered major proponents of their respective musical traditions and genres.

            All of the musical fragments in the test were twenty seconds in duration and were presented from an Apple MacBook connected to a loudspeaker system.  Musical fragments were selected in accordance to having a similar note density while also containing those musical elements associated with the emotions and moods of particular seasons.[7]

Procedure

            Listeners were tested as a group.  For Test 1, listeners were told that they would be played four musical examples, each twenty seconds in duration.  During the performance of each musical example, a slide show containing three photographs was displayed through an overhead projector.  Depicted in each of these photographs were individuals or groups of individuals expressing various emotional responses to environmental stimuli.  Listeners were asked to choose from the three photographs that picture which depicted or paralleled most accurately the emotion evoked by the musical example, and to write their selection on an answer sheet provided for them.  For each musical example different pictures were used. 

            For Test 2 listeners were told that they would be played a pair of musical examples, both being twenty seconds in duration and separated by a gap also twenty seconds in duration.  Listeners were asked to consider the emotions evoked by the musical examples, much like in the initial test.  On their answer sheets the listeners were provided with ten pairs of descriptive words for each pair of musical examples.  For each example, the listeners were asked to choose those paired words that they felt described best the relationship existing between the two musical examples.  Listeners were instructed to choose at least three and no more than six pairs of descriptive words.

Table 2

Musical Stimuli

Performer

Raga

Season

Genre

 

1

Deepak

Summer

Vocal (male)

2

Deepak

 

Vocal (male)

 

 

 

 

3

Miyan ki Malhar

Monsoon

Sitar (stringed)

4

Miyan ki Malhar

 

Sitar (stringed)

5

Miyan ki Malhar

 

Santoor (stringed)

 

 

 

 

6

Megh

Monsoon

Sitar (stringed)

7

Megh

 

Bansuri

 

 

 

 

3

Malkauns

Winter

Sitar (stringed)

5

Malkauns

 

Santoor (stringed)

8

Malkauns

 

Sarangi (stringed)

4

Malkauns

 

Sitar (stringed)

5

Malkauns

 

Santoor (stringed)

 

 

 

 

3

Shree

Winter

Sitar (stringed)

9

Shree

 

Sarod (stringed)

10

Shree

 

Vocal (male)

4

Shree

 

Sitar (stringed)

 

 

 

 

9

Hindol

Summer

Sarod (stringed)

4

Hindol

 

Sitar (stringed)

11

Hindol

 

Vocal (male)

12

Hindol

 

Sarod (stringed)

9

Hindol

 

Sarod (stringed)

 

 

 

 

3

Basant

Summer

Sitar (stringed)

13

Basant

 

Sarangi (stringed)

6

Basant

 

Sitar (stringed)

8

Basant

 

Sarangi (stringed)

 

Results

            Table 3 illustrates the rates of success at which listeners were able to correlate the musical examples with the photographs contained in Test 1.  Ragas representing each of the four seasons were tested, with the summer and winter ragas – Deepak and Malkauns respectively – yielding a significantly higher success rate in correct identification amongst listeners than the monsoon and spring ragas – Miyan ki Malhar and Hindol respectively.  

Table 3

Success Rates of Music and Emotional Correlation

Raga

Miyan ki Malhar

Deepak

Malkauns

Hindol

 

 

Corresponding

Season

Monsoon

Summer

Winter

Spring

 

 

Percent

0.35

0.85

0.85

0.28

 

            Table 4 shows the rates of success for listeners in perceiving and identifying correctly the relationship between the two musical examples in Test 2 (p < .0001).  The highest rate of success occurred between the pairing of the winter and spring ragas Malkauns and Basant.  However, the pairing of raga Malkauns with the other spring raga, Hindol, yielded the lowest percentage of correct answers among listeners.  This disparity implies that the perceived musical or psychophysical phenomenon giving raga Basant its “spring-ness” were more lucid than those tied to raga Hindol.  Between the two monsoon ragas tested – raga Miyan ki Malhar and raga Megh – the binary relationship between raga Megh and raga Deepak scored slightly higher in the test than that of raga Miyan ki Malhar and raga Deepak.  Similar to the winter–spring ragas, the data concerning the summer–monsoon ragas suggest the presence of musical or other psychophysical elements in raga Megh that evoke clearer or more discernible emotional cues than those present in raga Miyan ki Malhar.

 

Table 4

Success Rates of Identifying Paired Ragas with

Correct Descriptive Binaries

 

Raga Pair

Seasonal Connection

Percent

 

Deepak – Miyan ki Malhar

Summer – Monsoon

.13

 

Deepak – Megh

Summer – Monsoon

.16

 

Shree – Hindol

Winter – Spring

.13

 

Shree – Basant

Winter – Spring

.18

 

Malkauns – Hindol

Winter – Spring

.04

 

Malkauns – Basant

Winter – Spring

.24

 

            Each pair of summer–monsoon and winter–spring ragas was tested twice in this experiment – the order of the performance of paired ragas being switched between their first and second hearing.  In doing so, listeners were asked to judge the relationship of the pairs of ragas in antecedent–consequent order as well as in a reversed, and more convoluted consequent–antecedent order.

            Table 5 shows the results among listeners in identifying correctly the relationship between summer and monsoon ragas.  The data indicates that listeners had more success in identifying the correct binary relationship between these ragas if they occurred in an antecedent–consequent order (summer followed by monsoon).  Listeners had a higher success rate of identifying the relationship of raga Deepak and raga Megh than the relationship between raga Deepak and raga Miyan ki Malhar. 

Table 4

Identification of Summer – Monsoon Raga Binaries

Raga Pair

Seasonal Connection

Percent

 

Deepak – Miyan ki Malhar

Summer – Monsoon

.30

 

Miyan ki Malhar – Deepak

Monsoon - Summer

.22

 

Deepak – Megh

Summer – Monsoon

.42

 

Megh – Deepak

Monsoon – Summer

.19

 

            The data in Table 6 shows the results of the listeners in identifying correctly the binary relationship between winter and spring ragas.  Achieving the highest success rate of correct answers were the pairings of both of the winter ragas – Shree and Malkauns – with raga Basant.  Interestingly, the highest rates of success came when the ordering of these raga pairs involving raga Basant were framed in the reversed, consequent–antecedent manner (spring raga followed by winter raga).  In contrast, in the test questions involving raga Hindol as the spring raga, listeners were more successful in identifying the correct binary relationships when the raga pairs appeared in antecedent–consequent order.  The lowest rate of success among all winter–spring raga pairs occurred between raga Shree and raga Basant as they appeared in antecedent–consequent order.  This data suggests that for the winter–spring raga pairs involving raga Basant, listeners were more successful in perceiving an arbitrary relationship between the two ragas as compared to an antecedent–consequent relationship.  When raga Hindol appeared as the spring raga in the pair of ragas, listeners were more successful in identifying the correct binary relationship when they were presented as antecedent–consequent.

Table 5

Identification of Winter – Spring Raga Binaries

Raga Pair

Seasonal Connection

Percent

 

Shree – Hindol

Winter – Spring

.31

 

Hindol – Shree

Spring – Winter

.21

 

Shree – Basant

Winter – Spring

.09

 

Basant – Shree

Spring – Winter

.55

 

Malkauns – Hindol

Winter – Spring

.19

 

Hindol – Malkauns

Spring – Winter

.18

 

Malkauns – Basant

Winter – Spring

.37

 

Basant – Malkauns

Spring – Winter

.57

 

            Table 6 illustrates the percentage of correct associations with ragas and their correlating emotions as established by Indian theoretical treatises.[8]  Yielding the highest rate of successful correlation was raga Basant, while raga Hindol produced the lowest rate of success among the tested listeners.  Categorically, raga Megh was identified more successfully with those aesthetics of the monsoon ragas than raga Miyan ki Malhar; raga Shree was identified with the characteristics of winter ragas more so than raga Malkauns; and raga Basant was identified with a much greater deal of success to the qualities of spring ragas than raga Hindol 

Table 6

Identification of Ragas with Correct Corresponding Emotions

Raga

Seasonal Connection

Percent

Miyan ki Malhar

Monsoon

.26

 

Megh

Monsoon

.32

 

Deepak

Summer

.30

 

Shree

Winter

.33

 

Malkauns

Winter

.29

 

Basant

Spring

.42

 

Hindol

Spring

.17

 

General Discussion

            The results of this study support the first hypothesis made regarding the ability to detect emotions in an unfamiliar musical language.  Listeners’ responses in Test 1 indicate their sensitivity to the emotional cues of Hindustani music, despite their lack of familiarity with the musical tradition. 

            For Test 2, although the rates of success in listeners for identifying the correct binary relationship were less than half, the data reveals an interesting point about how listeners of unfamiliar music perceive music.  As shown by the summer–monsoon raga examples, listeners had a higher rate of success in identifying the correct terminologies describing the binary relationship between ragas when they appeared in antecedent–consequent order.  In the examples containing winter–spring ragas, listeners had a more successful rate of identifying the correct relationship of the pairings when they appeared in the arbitrary, consequent–antecedent order when raga Basant was the spring raga.  When raga Hindol was the spring raga, listeners had more success identifying the corresponding relationship when the ragas were presented as antecedent–consequent.

            From this data it can be determined that, even when faced with unfamiliar musical paradigms, listeners can perceive emotional cues inherent within musical forms, as well as sense an antecedent–consequent relationship between musical examples.  The lack of familiarity does not hinder the ability of listeners in perceiving emotional cues, as illustrated in the empirical study of Balkwill & Thompson.  However, in order to provide a more concrete account of the musical cues that people associate with feelings of emotion or of the antecedent–consequent relationship between musical examples, an investigation into the psychophysical cues would be helpful.  A similar experiment gauging the nature of the factors (musical or psychophysical) and their roles in influencing listeners’ decisions would assist in shedding light on the factors contributing to the success or failure regarding the correct identification of ragas.  In addition, the gauging of such phenomena would illustrate how and to what extent listeners perceived the “seasonal-ness” of individual ragas tested.

 

 

 

Appendix A

Ragas and their Corresponding Emotional Aesthetics

Ragas

Miyan Ki Malhar

Megh

Deepak

Malkauns

Shree

Basant

Hindol

Season

Monsoon

Monsoon

Summer

Winter

Winter

Spring

Spring

 

 

 

 

 

 

 

 

Emotions

 

 

 

 

 

 

 

Active

 

 

 

 

 

 

1

Agitated

 

 

1

 

 

 

 

Amorous

 

 

 

 

 

1

1

Austere

 

 

 

1

 

 

 

Balanced

 

1

 

 

 

 

1

Blue

 

1

 

1

1

 

 

Brilliant

 

 

 

 

 

1

 

Calming

1

1

 

 

 

 

 

Coarse

 

 

1

 

 

 

 

Comical

 

 

 

 

 

1

 

Common

 

 

 

 

 

 

1

Contemplative

 

 

 

1

 

 

 

Deep

 

1

 

 

 

 

 

Dry

 

 

1

 

 

 

 

Dull

 

 

 

1

1

 

 

Dynamic

 

 

 

 

 

 

1

Elegant

 

 

 

 

 

1

1

Fiery

 

 

 

 

 

 

1

Frowning

 

 

1

 

 

 

 

Gentle

 

 

 

 

1

 

 

Gold

 

 

 

 

 

 

1

Graceful

 

 

 

 

 

1

 

Grave

 

 

 

1

1

 

 

Happy

 

 

1

 

 

 

 

Hard

 

 

 

 

 

 

1

Indifferent

 

 

 

1

 

 

 

Irritating

 

 

1

 

 

 

 

Joyful

 

 

1

 

 

 

 

Lackluster

 

 

 

1

1

 

 

Lively

 

 

 

 

 

 

1

Loving

 

 

 

 

 

 

1

Meditative

 

 

 

1

 

 

 

Melancholy

1

1

 

 

 

 

 

Old

 

 

 

1

1

 

 

Passionate

 

 

 

 

 

 

1

Pensive

 

 

 

1

 

 

 

Playful

 

 

 

 

 

1

 

Radiant

 

 

 

 

 

1

 

Rainy

1

1

 

 

 

 

 

Relaxed

1

1

 

 

 

 

 

Relieving

1

1

 

 

 

 

 

Rigid

 

1

 

 

1

 

1

Royal

 

 

 

 

1

 

 

Sad

1

 

 

 

 

 

 

Serious

 

 

 

1

1

 

 

Shallow

 

 

1

 

 

 

 

Silver

 

 

 

 

1

 

 

Smiling

 

1

 

 

 

 

 

Smooth

1

1

 

 

 

 

 

Soft

 

 

 

 

1

 

 

Soothing

1

 

 

 

 

 

 

Spring

 

 

 

 

 

1

1

Stale

 

 

 

 

1

 

 

Stiff

 

 

 

 

1

 

 

Stoic

 

 

 

1

1

 

 

Suffering

 

 

1

 

 

 

 

Sunny

 

 

1

 

 

 

 

Tense

 

 

1

 

 

 

 

Thick

 

1

 

 

 

 

 

Thin

 

 

1

 

 

 

 

Unequal

 

 

 

1

 

 

 

Uneven

 

 

 

1

 

 

 

Well-Shaped

 

 

 

 

 

 

1

Wet

1

1

 

 

 

 

 

Winter

 

 

 

1

1

 

 

Yellow

 

 

1

 

 

 

1

Young

 

 

 

 

 

1

1

 

 

 

 

 

 

 

 

 

 

References

Ali, S. Omar and Zehra F. Peynircioglu.  “Intensity of Emotions Conveyed and Elicited             by Familiar and Unfamiliar Music.” Music Perception: An Interdisciplinary             Journal Vol. 27, No. 3 (February 2010): pp. 177-182.

 

Behrens, G. A., and Green, S.  “The Ability to Identify Emotional Content of Solo             Improvisations Performed Vocally and on Three Different Instruments.”                          Psychology of Music 21 (1993): pp 20-33.

 

Castellano, Mary, Jamshed Bharucha, and Carol Krumhansl.  “Tonal Hierarchies in             the Music of North India.” Journal of Experimental Psychology General 113             (1984): pp 394-412.

 

Danielou, Alan.  The Ragas of Northern Indian Music.  New Delhi: Munshiram             Manoharlal Publishers, 1991.

 

Deva, B. Chaitanya, and K. G. Virmani.  “A Study in the Psychological Response to             Ragas.”  Research Report II of Sangeet Natak Akademi. New Delhi: Indian             Musicological Society, 1975.

 

Gabrielsson, Alf, and Juslin, Patrick.  “Emotional Expression in Music Performance:             Between the Performer’s Intention and the Listener’s Experience.”  Psychology             of Music 24 (1996): pp. 68-91.

 

Haack, Paul.  “The Behavior of Music Listeners.” In D. Hodges (Ed.), Handbook of

            Music Psychology. Lawrence, KS: National Association for Music Therapy, 1980             (pp.141-182).

 

Hoshino, Etsuko.  “The Feeling of Musical Mode and its Emotional Character in a             Melody.”  Psychology of Music, 24 (1996): pp. 29-46.

 

Jairazbhoy, Nazir.  The Rags of North Indian Music: Their Structure and Evolution.              Bombay: Popular Prakashan, 1995.

 

Kaufmann, Walter.  The Ragas of North India.  Bloomington: Indiana University             Press, 1968.

 

Kessler, Edward, Christa Hansen, and Roger Shepard.  “Tonal Schemata in the             Perception of Music in Bali and in the West.” Music Perception 2 (1984): pp             131-165.

 

Khan, Ali Akbar and George Ruckert.  The Classical Music of North India: The Music of             the Baba Allaudin Gharana as Taught by Ali Akbar Khan at the Ali Akbar College             of Music.  New Delhi: Munshiram Manoharlal, 2004.

 

Kratus, John.  “A Developmental Study of Children’s Interpretation of Emotion in             Music.”  Psychology of Music (1993): pp. 21, 3-19.

 


[1] G. A. Behrens and Green, S, “The Ability to Identify Emotional Content of Solo Improvisations Performed Vocally and on Three Different Instruments,” Psychology of Music 21 (1993); Paul Haack, “The Behavior of Music Listeners,” in D. Hodges (Ed.), Handbook of Music Psychology (Lawrence, KS: National Association for Music Therapy, 1980); John Kratus, “A Developmental Study of Children’s Interpretation of Emotion in Music,” Psychology of Music (1993).

[2] Ali, S. Omar and Zehra F. Peynircioglu, “Intensity of Emotions Conveyed and Elicited by Familiar and Unfamiliar Music,” Music Perception: An Interdisciplinary Journal Vol. 27, No. 3 (February 2010); Edward Kessler, Christa Hansen, and Roger Shepard, “Tonal Schemata in the Perception of Music in Bali and in the West,” Music Perception 2 (1984); Mary Castellano, Jamshed Bharucha, and Carol Krumhansl, “Tonal Hierarchies in the Music of North India,” Journal of Experimental Psychology General 113 (1984).

[3] For a list of all associated emotions with the ragas tested in this experiment see Appendix A at the end of this paper.

[4] See Walter Kaufmann, The Ragas of North India (Bloomington: Indiana University Press, 1968).

[5] One of the answer sheets submitted was filled in incorrectly, yielding fourteen items of data to be analyzed.

[6] For a description of the elements of a Hindustani performance see Nazir Jairazbhoy, The Rags of North Indian Music: Their Structure and Evolution (Bombay: Popular Prakashan, 1995).

[7] See Ibid.; Kaufmann, The Ragas of North India; and Ali Akbar Khan and George Ruckert, The Classical Music of North India: The Music of the Baba Allaudin Gharana as Taught by Ali Akbar Khan at the Ali Akbar College of Music (New Delhi: Munshiram Manoharlal, 2004). 

[8] See Appendix A.

Thursday
Dec122013

Ben Negley: Beat Mapping in Orchestral Music: An Empirical Exposition

 

 

Introduction

            Increasingly sophisticated recording technology allows for not only the propagation and individualization of recorded music, but also the opportunity to study the styles of conductors, orchestras and performers with empirical performance evidence as well as the primary documents, reviews, and scores that have traditionally been the foundations of musical historiography. Conductors and performers can no longer rely only on these latter documents for prescriptions of performing style, and musicologists must recognize the changing narrative of the musical work via performance and recording. In sampling and analyzing a large amount of data from recordings and using only empirical data–as opposed to journalistic surveys of the styles and tempi of conductors–this study analyzes a small section of Mahler’s Second Symphony in search of answers not available in the score.

            Studies of tempo and duration have exploded over the last twenty years. This explosion is perhaps not only the result of better recording technology and the higher number of recordings on the market, but also the exponential improvements in computing power. Computers allow for vast amounts of data to be collected, stored, represented and analyzed cheaply and efficiently, and most empirical studies of recordings rely on computers for data storage and analysis. Nonetheless, earlier studies in duration are still of interest.

            T.C. York’s book from 1929, How Long Does it Play offers timings for the most popular symphonies, concertos, overtures, and other orchestral works performed during the early 20th century.[1] Certainly not intended as any sort of scholarly study, but rather as a practical guide for conductors and concertgoers, York’s timings are rounded to the nearest minute and taken from the performances of a variety of premier British conductors. A similar guide, if released today, would most likely include all of Mahler’s symphonies, but York’s guide only includes timings for the First and Fourth Symphonies, two of Mahler’s shortest and most frequently performed works. In contrast, Solomon Aronowsky’s gigantic Performing Times of Orchestral Works, published in 1959, includes all of Mahler’s Symphonies as well as many individual movements that can be programmed as stand-alone works. But, like York, Aronowsky gives little indication as to the sources of his timings and alludes only to timing many concerts, and striking a “happy average” in each printed duration.[2] Concerning Mahler’s Second Symphony, Aronowsky’s published duration is 85 minutes­–a slightly longer than average timing–but the reader has no way of knowing if the usual silences between movements are included or not.[3] Thus, while Aronowsky’s timing does indicate to the listener a ballpark figure, it ignores the striking variety in a work like Mahler’s Second, and misrepresents the almost thirty minute difference between the longest and shortest recording of the work.[4]

British conductor and composer Sir George Smart, who was a personal acquaintance of Beethoven’s, timed himself conducting a variety of works between 1819 and 1843. One is immediately tempted to hold up Smart’s timings as evidence of a by-gone glory era (he knew Beethoven!), but his timings pose more questions than answers. Smart leaves no indications of repeats, spaces between movements, and in some cases he fails to even mention the particular work being timed. Nicholas Temperley describes these problems in his 1966 article “Tempo and Repeats in the Early Nineteenth Century.”[5] In what must have been one of the first articles of its kind, Temperley compares Smart’s timings with the timings of recordings. In speculating on the types of repeats Smart took in his timed works, Temperley gauges Smart’s timings with timings of recordings with varying repeat usage. Comparing Smart’s timings to timings recorded in the 1960s, Temperley is able to speculate on what types of repeats Smart took. Smart’s durations vary from the recordings studied by Tempereley, but elicit no discernible regression towards longer or shorter durations. 

A variety of more recent studies have carefully examined durations and tempi. The work of José Antonio Bowen is central to this study and the basis on which its methodology is constructed. In his 1996 article “Tempo, Duration, and Flexibility: Techniques in the Analysis of Performance” Bowen introduces a variety of techniques in the analysis of recorded music.[6] Building from Ingarden’s distinction between performances, scores, and musical works, Bowen seeks to examine recordings as entities separate from their associated scores. Using various modes of graphical representation, Bowen demonstrates how the analysis of recorded performances can be used as a means to a variety of ends. Several of Bowen’s conclusions are particularly poignant and are reproduced below:[7]

 

1. While detailed listening to individual performances is crucial, historical investigations of performance tradition must use data sets as large as possible.

 

3. Tempo data should be measured in the most accurate way possible and on the smallest level.

 

            Some of the most interesting recent work in empirical studies of recordings has been done by CHARM in London, a research group that was directed by Nicholas Cook and appears to have gone defunct in 2009.[8] Using a variety of software applications, CHARM focused on the tempo, duration, dynamics and style in recordings, and helped develop Sonic Visualizer Software, which allows for many different types of recording analysis and was used extensively in this project.

 

Methodology

My methodology builds on the work of Bowen, and focuses on a single orchestral work with a huge variety of recordings. Quantitatively describing the placement of individual beats, or ‘beat–mapping’, is achieved somewhat easily with Sonic Visualiser software.[9] Though several VAMP plug-ins intended to automatically map beat onsets are available for Sonic Visualiser, they work very poorly for orchestral music, and the beat–mapping must be achieved manually for maximum accuracy. Once an audio track is imported to Sonic Visualizer, mapping is achieved by depressing a key on the keyboard as the beats are encountered. Once annotations are placed, the annotation layer can be exported and copied into spreadsheet or statistical software.

To test various applications of this mode of beat mapping, I collected data from two sections of the first movement of Mahler’s Second Symphony and applied the data for two separate tests. The first compares the rallentando in the climactic measures in both instances, while the second examines two recordings by Pierre Boulez, and correlations between beat placement within a measure, and beat durations.

            In the first test, I mapped the beats for two very similar instances in the first movement of Mahler’s Second Symphony. I consider the first instance to be a part of the exposition and the second to be a part of the recapitulation. Both instances culminate with climaxes that are typically characterized by large-scale rallentando. In the first instance, the rallentando is followed by a dotted funeral-march theme:

 

In the second, the climax is defused by a quick decrescendo in lieu of the dotted march theme:

 

 

The entire movement is in common time, and in mapping the beats of both of these sections, I hoped to be able to compare two nearly identical sections of music with very sharply contrasting formal significance. Eight recordings were sampled, including two by Pierre Boulez, two by Otto Klemperer, two by Leonard Bernstein, one by Hermann Scherchen and one by Simone Young. These were somewhat arbitrarily chosen, based on the recordings available at my disposal, but I chose multiple recordings by single conductors to explore how conductors change not only from exposition to recapitulation, but years.       

            The second test involved a larger set of data, but only the two recordings of Boulez. These two recordings are particularly interesting because the first was recorded in 1973 and the second as part of a cycle in 2005. Thus, this test considers Boulez’s style over a 32–year period, using beat-length data from 28 measures in each the exposition and recapitulation of the same movement of Mahler’s Seconds Symphony. Instances 1 and 2 are measure 23, in the exposition and recapitulation of these test 2 samples, respectively.

 

Results

 

Test 1

            The beat durations for the five beats tested in Test 1 are displayed below:

 


1(1)

2(2)

3(3)

4(4)

1(5)

boulez bbc(1)

0.891065759

1.024580499

1.166802721

1.767619048

0.833015873

boulez bbc(2)

0.873514739

1.056258503

1.046349207

1.663854875

0.873469388

boulez vpo(1)

0.832040816

0.807687075

1.26430839

1.631496599

0.816462585

boulez vpo(2)

0.897800453

0.963628118

0.89829932

1.904036281

0.634217687

scherchen(1)

0.915011338

0.892743764

1.005759637

0.864943311

0.806893424

scherchen(2)

1.023492064

1.10478458

1.044739229

1.123265306

0.777868481

klempvso(1)

0.836734694

0.787755102

0.938775511

0.679614512

0.815396825

Klempvso(2)

0.761587301

0.750861678

0.920997733

0.581587301

0.551473923

young(1)

0.70367347

0.751836734

1.048163266

0.895873016

0.722721088

Young(2)

0.639886622

0.703968254

0.806031746

0.7061678

0.663809524

bernstein 63(1)

1.071655329

1.048049887

1.232108843

1.184013606

0.95569161

bernstein 63(2)

1.000090703

1.119773242

1.463854876

1.584285714

0.751995465

klempphil(1)

0.751950113

0.774058957

0.722312925

0.722585034

0.749092971

Klempphil(2)

0.845578231

0.762743764

0.722970522

0.916530612

0.792176871

bernstein 87(1)

1.098276644

1.211292517

1.340680272

1.059319728

1.044399092

bernstein 87(2)

0.952517007

0.902993197

0.867188209

1.03707483

0.96569161

 

            In this table, the printed durations correspond to the difference between the beat listed at the header of the column and the beat listed at the header of the next column. Thus, the length of Young’s beat 1 in Instance 1 is about .7 seconds. Or, the difference between Young’s 1st and 2nd beat in this Instance is .7 seconds.

One would expect beat 4, being the climactic rallentando beat, to typically be the longest, with beats 1 through 3 serving as a gradual rallentando. But this is not always the case. Boulez’s two recordings–recorded more than 30 years apart–both represent dramatic elongation of beat 4 in all instances. But most of the other sampled recordings elongate beat 3 in relation to beat 4 in at least one of two instances. Klemperer’s Vienna Symphony recording is dramatically different than Boulez’s recordings because in both instances beat 3 is elongated at the expense of beat 4. This is not the case in Klemperer’s later Philharmonia Orchestra recording, though, where neither beat is longer than the other in the 1st instance, but beat 4 is longer than its predecessor in the 2nd.

The chart below characterizes the elongation of beat 4 in relation to the elongation of beat 3:



boulez bbc(1)

0.600816327

boulez bbc(2)

0.617505668

boulez vpo(1)

0.367188209

boulez vpo(2)

1.005736961

scherchen(1)

-0.140816326

scherchen(2)

0.078526077

klempvso(1)

-0.259160999

Klempvso(2)

-0.339410432

young(1)

-0.15229025

Young(2)

-0.099863946

bernstein 63(1)

-0.048095237

bernstein 63(2)

0.120430838

klempphil(1)

0.000272109

Klempphil(2)

0.19356009

bernstein 87(1)

-0.281360544

bernstein 87(2)

0.169886621

 

Interestingly, in every example except Klemperer’s Vienna Symphony recording, the elongation of beat 4, relative to beat 3, increased from the 1st instance to the 2nd. This increase suggests a relative increase in elongation in the 2nd incident compared to the 1st, or a relatively more emphasized climax. This relative increase is contextualized by the fact that in terms of differences between beats 4 in both incidents are only positive in half of the recordings:

 

boulez bbc(1)

-0.103764173

boulez bbc(2)


boulez vpo(1)

0.272539682

boulez vpo(2)


scherchen(1)

0.258321995

scherchen(2)


klempvso(1)

-0.098027211

Klempvso(2)


young(1)

-0.189705216

Young(2)


bernstein 63(1)

0.400272108

bernstein 63(2)


klempphil(1)

0.193945578

Klempphil(2)

 

bernstein 87(1)

-0.022244898

bernstein 87(2)

 

 

            Thus, although the elongation of beat 4 relative to beat 3 increased from the first instance to the second in 7 of the 8 recordings, the actually length of beat 4–the climactic upbeat–only increased in half of the recordings.

 

Test 2

            In looking for correlations between beat position in the measure (1,2,3,4) and beat length, the second test examines the expressive style of Pierre Boulez in two recordings spanning 32 years. Correlations between beat position and beat duration, by measure, are reproduced below, with the positive correlations highlighted in gray:

 

measure

bbc(1)

bbc(2)

vpo(1)

vpo(2)

1

0.63

-0.67

-0.61

0.39

2

-0.89

0.22

-0.86

0.77

3

-0.26

0.67

0.58

0.75

4

0.08

-0.98

-0.23

-0.19

5

-0.45

-0.08

0.98

-0.50

6

-0.56

0.62

0.80

-0.26

7

-0.37

0.31

0.65

0.65

8

-0.07

-0.41

0.11

-0.90

9

-0.55

-0.59

-0.63

-0.14

10

0.64

0.64

-0.18

-0.98

11

-0.24

-0.41

-0.82

0.01

12

-0.69

-0.17

-0.12

0.87

13

0.33

-0.63

-0.44

0.73

14

-0.82

-0.74

-0.06

-0.23

15

0.37

-0.83

-0.36

-0.35

16

-0.25

-0.69

-0.23

0.71

17

-0.36

-0.79

-0.94

0.54

18

0.71

-0.09

-0.99

-0.60

19

0.25

-0.28

-0.85

-0.38

20

0.51

-0.56

0.04

0.22

21

-0.54

0.92

-0.50

-0.08

22

0.32

0.64

-0.06

0.83

23

0.93

0.88

0.94

0.77

24

-0.62

-0.80

-0.82

-0.69

25

-0.93

0.45

0.11

0.45

26

0.15

0.45

-0.36

0.54

27

0.05

0.05

0.62

0.42

28

-0.85

0.87

0.85

0.99

 

            As expected, the only measure with strong positive correlations in both instances in both recordings is measure 23, which is the climactic measure highlighted in Test 1. Thus, these correlations do not appear to suggest a pattern in Boulez’s style, at least in this short, 28-measure passage.

            The averages and standard deviations for the beat lengths are presented below:

 


1

2

3

4

boulezbbc(1)avg

0.75

0.76

0.76

0.77

boulezbbc(1)stdev

0.05

0.07

0.10

0.20






boulezbbc(2)avg

0.70

0.68

0.70

0.71

boulezbbc(2)stdev

0.06

0.08

0.10

0.20






boulezvpo(1)avg

0.72

0.70

0.72

0.74

boulezvpo(1)stdev

0.06

0.05

0.13

0.18






boulzevpo(2)avg

0.68

0.68

0.67

0.74

boulezvpo(2)stdev

0.05

0.06

0.06

0.24

 

            In every case, beat 4 has a higher average and standard deviation, but these facts are probably at least partly explained by the dramatic elongation of beat 4 in measure 23.

 

General Discussion

 

            The two experiments conducted above did not necessarily yield important conclusions on the nature of tempo rubato, or beat emphasis in recordings by Boulez, but I believe that similar analytical techniques applied to larger sample sizes or different musical excerpts could produce interesting results as to not only the nature of expressive interpretation, but also the importance of the conductor and her gestures. What if, through empirical studies of recordings, we could compare the styles of conductors beyond the journalistic opining that makes up the majority of the literature on conductors and conducting? And, what if quantitative comparisons of several recordings by a single conductor, as in my second test, yielded no discernable empirical similarities? What if we could quantitatively show which conductors maintained salient expressive characteristics over multiple recordings and which ones did not? I believe that empirical studies of tempo and duration in recordings could be one of the first steps in de-mystifying conducting and an integral part of a well-needed critical ethnography of the conductor.

 

Bibliography/Discography

 

Aronowsky, Solomon. Performing Times of Orchestral Works. London: Ernest Benn Limited, 1959.

 

Bernstein, Leonard. New York Philharmonic Orchestra. Deutsche Grammophon, 423 3952, 1988, compact disc. Recorded in 1987.

 

_______________. New York Philharmonic Orchestra. Sony, SM2K 89499, 2001, compact disc. Recorded in 1963.

 

Boulez, Pierre. Vienna Philharmonic Orchestra. Deutsche Grammophon, DG 477 6004, 2006, compact disc. Recorded in 2005.

 

___________. BBC Symphony Orchestra. Originals, 855, 1995, compact disc. Recorded in 1973.

 

Bowen, José Antonio. “Tempo, Duration, and Flexibility: Techniques in the Analysis of Performance.” The Journal of Musicological Research 16 (1996): 111-156.

 

Cannam, Chris, Christian Landone, and Mark Sandler. “Sonic Visualiser: An Open Source Application for Viewing, Analysing, and Annotation Music Audio Files.” http://www.sonicvisualiser.org/.

 

Klemperer, Otto. The Philharmonia Orchestra. EMI, EMI 724356725553, 1963, compact disc. Recorded in 1961-2.

 

_____________. Vienna Symphony Orchestra. Testament, JSBT 2 8456, 2010, compact disc. Recorded in 1951.

 

Scherchen, Hermann. Vienna State Opera Orchestra. Theorema, MCAD2-9833, 1993, compact disc. Recorded 1958.

 

Temperley, Nicholas. “Tempo and Repeats in the Early Nineteenth Century.” Music and Letters 47, No. 4 (Oct., 1966): 323-336.

 

York, T.C. How Long Does it Play: A Guide for Conductors. London: Oxford University Press, 1929.

 

Young, Simone. Hamburg Philharmonic Orchestra. OEHMS, OC412, 2010, compact disc. Recorded in 2010.

 

 


[1] T. C. York, How Long Does it Play: A Guide for Conductors (London: Oxford University Press, 1929).

[2] Solomon Aronowsky, Performing Times of Orchestral Works (London: Ernest Benn Limited, 1959), ix.

[3] Aronowsky, Performing Times of Orchestral Works, 452.

[4] Of the 52 recordings analyzed in my master’s thesis (Ben Negley, Variability in Mahler’s Second Symphony: An Empirical Approach, Unpublished, 2012), Otto Klemperer’s 1950 account with the Sydney Symphony Orchestra was the shortest at 66 minutes and 40 seconds and Hermann Scherchen’s 1958 Vienna State Opera Orchestra recording was the longest at 93 minutes, 07 seconds.

[5] Nicholas Tempereley, “Tempo and Repeats in the Early Nineteenth Century,” Music and Letters 47, No. 4 (October, 1966): 323-336.

[6] José Antonio Bowen, “Tempo, Duration, and Flexibility: Techniques in the Analysis of Performance,” The Journal of Musicological Research 16 (1996): 111-156.

[7] Bowen, “Tempo, Duration, and Flexibility: Techniques in the Analysis of Performance,” The Journal of Musicological Research, 145, 146.

[8] See http://www.charm.kcl.ac.uk/index.html

[9] Sonic Visualizer software is available for free download at http://www.sonicvisualiser.org/.

Sunday
Dec012013

Ben Negley: Mapping Tempo Rubato

Hello all, just wanted to give you an idea of what I’m working on and maybe prime you for my presentation on Tuesday.

Although many studies encounter music perception and cognition empirically, there appears to be a very small amount of empirical, comparitive studies on recordings. Recordings are typically compared based on subjective observations, and conclusions are drawn not from quantitative measurements, but from qualitative ones. Magazines, blogs, journals, etc empower critics to make opinions on performances and recordings, but I wonder how interpretations can be studied quantitatively, and in what ways quantitatve studies can augement qualitative ones.

For this study, I’ve decided to focus on two instances of tempo rubato within Mahler’s Second Symphony. Both instances are basically the same in terms of notes and rhythms, but the first is in the exposition and the second is in the recapituation; you can hear them in this Youtube video, at 2:10 and 16:15, respectively.

https://www.youtube.com/watch?v=XSgrm4R1yh8

I picked this Gergiev performance as an example because the big slow-down on beat four in both cases is pretty dramatic, and if I were to time the difference between the onset of the climactic beat 4 and beat 1 of the next measure it would be much longer than the distance between said beat 1 and its following beat 2. But this isn’t always as true as it is in Gergiev’s performance, and in some recordings, almost no tempo rubato is observed at all. In this 1950 recording, Otto Klemperer uses much less rubato, creating (in my opinion) a different effect. You’ll hear it at 1:40:

https://www.youtube.com/watch?v=OQLIk3jaa1k

In my study I’ve timed the beat-beat differences in these two almost identical instances in 7 recordings (there may be more to come before Tuesday…) to highlight the unique approaches to rubati of difference conductors, but also to examine the relationship between these two rubato episodes in the individual recordings.

In my presentation on Tuesday, I’ll show charts detailing various approaches to these climaxes and discuss quantitative relationships between the two phrases.

Friday
Nov292013

Cameron Mozee-Baum - Prospectus

Cameron Mozee-Baum Project Materials for Empirical Study

“Does listening to music alter our perception of how a probe tone fits with a major scale?”

 

1. Written instructions & response pages (.pdf)

2. Complete example study sound file (link to SoundCloud)

 

 

Thursday
Nov282013

Experimental Design — Michael Lindsey

I’m publishing this a little early because I’m planning on going out of town for the next day and a half or so and wouldn’t have a chance to before Saturday evening.  Here’s what I have typed out for my study this Monday (or Wednesday).   I have some attachments for the procedue (I have a powerpoint file with the photos for the first test and a word document for the pairs of adjectives in the second test) but was unable to upload them to this posting.  I also have a lot of short musical files that I’ve put into an iTunes playlist (hence the track numbers besides each stimulus) that I was unsure of how to link up as well.  

 [[BC: the editing suggestions are visible in your text via boldface (for suggested alternative language) and some strikethrough. My comments are in double brackets, and usually in italics. I want to stress that these are just suggestions—please feel free to ignore them, as you know best what your stimuli and questions require.]]

METHOD/PROCEDURE OUTLINE

Test 1:  Detecting test subjects’ abilities to associate emotions with music. [[BC: but you probably won’t include this in anything that the participants see, right?]]

Prompt

In this experiment you will answer four questions regarding your perception of emotional affect in music [[<—first sentence not necessary; you’re inviting pre-test analysis]].  In each of the questions you will be played a single musical example, of 20 seconds in duration.  During each musical example you will be shown three pictures.  Each picture portrays a scene in which individuals or groups of individuals are expressing various emotions in reaction to various external stimuli.  Based on your perception of the musical examples, choose which picture best expresses or parallels the emotion or emotions evoked by the musical example.  For example, if you think that the music evokes a sad or melancholy emotion, choose the picture that you think most clearly identifies with sadness or melancholy.  Likewise, if the emotion you feel the music portrays is happy and vibrant, choose the best appropriately corresponding picture.  Write down the letter of the picture you feel best reflects your feeling or emotion in response to the emotion evoked by the musical example in the spaces provided on your answer sheet.

 

Stimulus 1 – Track 1 Raga Mian Ki Malhar (MKM1SitarAR) – C

Stimulus 2 – Track 3 Raga Deepak (Deepak3VocalAR) – B

Stimulus 3 – Track 5 Raga Malkauns (MalkaunSitarAR) – C

Stimulus 4 – Track 7 Raga Hindol (HindolSarodAR) – A

 

Test 2: Relating perceived emotions in contrasting musical stimuli.

Prompt

In this experiment you will be played a series of paired musical examples.  You will first hear a musical example 20 seconds in duration, followed by a pause of 20 seconds, and continued  followed by a second musical example also 20 seconds in length.  You will then be given 90 seconds in which to select a series of answers regarding the musical examples.  As in the first experiment, consider the emotions that are evoked by your feelings or emotions in response to the different musical examples, and the relationships that you perceive between the individual pairs of musical examples. 

After hearing each pair of musical passages? stimuli, choose a pair of words from the list given below, that that you feel reflect a relationship corresponding to the two musical examples that you heard.  [[Can you simplify this sentence?—>]] In these lists, the adjectives on the left indicate an emotion evoked by the first musical example while the adjective on the right identifies to the emotion evoked by the musical example heard second. [[Or how about “Choose a word from the list on the left to correspond to the first example, and a word on the right that relates to your first chosen word in the way that the second musical example related to the first.”]] Circle at least 3, and up to 6, sets of adjectives [[words?]]  for each pair of musical stimuli.  Circle at least 3. 

 

Stimulus 1 – Tracks 9 and 11 Raga Miyan Ki Malhar and Raga Deepak

Stimulus 2 – Tracks 13 and 15 Raga Basant and Raga Malkauns

Stimulus 3 – Tracks 17 and 19 Raga Shree and Raga Hindol

Stimulus 4 – Tracks 21 and 23 Raga Deepak and Raga Megh

Stimulus 5 – Tracks 25 and 27 Raga Malkauns and Raga Hindol

Stimulus 6 – Tracks 29 and 31 Raga Shree and Raga Basant

Stimulus 7 – Tracks 33 and 35 Raga Hindol and Raga Shree

Stimulus 8 – Tracks 37 and 39 Raga Megh and Raga Deepak

Stimulus 9 – Tracks 41 and 43 Raga Deepak and Raga Miyan Ki Malhar

Stimulus 10 – Tracks 45 and 47 Raga Hindol and Raga Malkauns

Stimulus 11 – Tracks 49 and 51 Raga Basant and Raga Shree

Stimulus 12 – Tracks 53 and 55 Raga Malkauns and Raga Basant