Performing BrahmsSimilarities in cello playing styles on record

30 May 2007 Centre for Digital Music Seminar, Queen Mary, University of London

2

Structure of talk• Computational (or systematic) musicology

– sound analysis

• Performing Brahms– a preliminary musicology user report of the C4DM

existing tools for analysing musical processing in sound

• What can be helpful?– from the OMRAS2 existing tools of both Goldsmiths &

Queen Mary (for the time being) with further...

3

Useful links• OMRAS2: Online Music

Recognition And Searching, C4DM, Queen Mary & Goldsmiths Digital Studios

• CHARM: The AHRC Research Centre for the History and Analysis of Recorded Music

• CPS: Centre for Performance Science, Royal College of Music

• IMR: Institute of Musical Research, University of London

4

Working title• Cello Performing Tradition on Record:

A Sound Analysis

• keywords artists similarity

cultural history

legendary cellists

machine learning

music perception

J.S.Bach BWV1007 iv & v; Chopin Op.3; Brahms Op.99 ii; Prokofiev Op.115 ii

5

Some key readings

Eric Clarke and Nicholas Cook, 2004 [eds.] Empirical Musicology: Aims, Methods, Prospects. (Oxford: OUP)

John Rink, 1995 [ed.] The Practice of Performance: Studies in Musical Interpretation (Cambridge: CUP)

Bruno Repp, 1992-2002 articles in Journal of Acoustical Society of America

ICMPC and ISMIR proceedings

Analysing Musical Sound

7

Previous works on sound analysis• Music psychologists

– Intonation, vibrato (Seashore et al 1934)– Timing and dynamic (Clarke 1984, 1995; Windsor and

Clarke 1997; Repp 1992, 1998, 1999)

• Music IR engineers– Timing (Dixon 2001, 2003 etc)– Ornaments (Casey and Crawford 2004)

• Musicologists– Timing (Bowen 1996, 1999; Cook 1987, 1995, 2002)– Portamento (Leech-Wilkinson 2006)

8

Sound analysis: similarities & ...

Music Psychology Music IR Musicology

Materials produce own recordings from lab existing audio recordings existing audio recordings

Measurement significant interests as a demonstration of "tool" some interests

Interpretation some interests not so much interested significant interests

Perception human "listening" participants machine listening interaction between his "own"

and machine listening

Statistics Qualitative and Quantitative data with ref to "tool" development data related

Tool development not interested most significant interests depends on…

Collaborations performing musicians industry computer programmers?

non-musicians musicologists

9

The role of systematic musicologists

• Measuring behavioural aspects of artists from musical processing (rhythm, loudness, pitch) in sound

• Interpreting the captured data in musical language and context

• Quantitative audio data handling -- statistical analysis

• bridging a gap between musicology and music IR

10

Computational tools used:• Sonic Visualiser

– Timing fluctuation in a global frame: reverse conducting – Measurement of performance hierarchy intensity level (data

can also be obtained through the Praat).

• BeatRoot– Rhythmic irregularity in a (repeat) performance structure:

IOI (performed score duration)

• MATCH– Alignment of all the investigated recordings

Performing Brahms

12

Investigated recordings: Brahms, Op.99, ii

Dates Artists Label Mvt duration Tempo

1936 Casals (cello) Horszowski (piano) HMV DB3059/62 08:01 Q = 401966 Piatigorsky (cello) Rubinstein (piano) RCA Victor 09026 62592 2 07:44 Q = 441968 Du Pré (cello) Barenboim (piano) EMI 7 63298 2 07:31 Q = 421985 Yo-Yo Ma (cello) Ax (piano) RCD!-7022 07:45 Q = 44

1986 Rostropovich (cello) Richter (piano) 410 510-2 GH 08:27 Q = 38

1999 Maisky (cello) Gililov (piano) 458 677-2 GH 06:50 Q = 50

Artists and Styles

14

Relationship tree of cellists

E Feuermann1902-1942

P Casals1876-1973

J Du Pre1945-1987

M Rostropovich1927-2007

G Cassado1897-1966

M Maisky1948-

M Gendron1920-1990

W Pleeth1916-1999

A direct lineage of teacher-student relation

G Piatigorsky1903-1076

J Klengel1859-1933H Becker

1864-1941

B Harrison1892-1965

Y-Y Ma1955-

L Rose1918-1984

A Ivashkin1948-

15

Casals on dynamic shaping

• Casals’ principle on dynamic phrasing– Melodic contour and

dynamic shaping: pitch higher - dynamic louder

– always exceptional cases

• Sibelius algorithm Espressivo– pitch higher - dynamic

louder

Sibelius algorithm

CasalsdB

16

Casals’s rubato

• For Casals, the “authentic” tempo is impossible (Corredo 1956: 123).

• The tempo should vary with the performer according to the circumstances.

• “swing-like”: flexible and precise

Bar 13 & 21, Menuet I, J.S.Bach BWV1007,

Casals’s performed score duration: IOI data captured through BeatRoot.

17

Reverse conducting: Sonic Visualiser

• Sonic Visualiser see http://www.charm.rhul.ac.uk/content/svtraining/intro.html

• Timing see http://www.soton.ac.uk/~musicbox/charm5.html

Brahms, Op.99, ii, Y-Y Ma

18

Beat level analysis: repeat structure

Casals (1936), J.S.Bach BWV1007 Sarabande: data captured through Sonic Visualiser

0

0.5

1

1.5

2

2.5

bars

IOI:

seco

nd

s

1b_1 2b_1 3b_1 1b_r 2b_r 3b_r

1 95 13

19

Beat tracking -- BeatRoot

• Inter-onset-interval IOI: performed score duration

• The user can manually correct machine errors

• provides measurement readings

Brahms, Op.99, ii, b1-19, Du Pré

20

Rhythmic irregularities

Casals (1936), J.S.Bach BWV1007 Sarabande: data captured through BeatRoot

0

50

100

150

200

250

300

350

1st repeat

2/1 /2 6/2 /3 7/1 13/3 14/1

IOI:

ms

bars/beat

Performing Brahms

22

Grouping structure: Brahms’s Op.99, ii

Phrase Grouping Key Boundary CadenceA: b1-11 4+4+3 F# major I-V HC

b12-19 4+4 F# major I-V HCB: b20-28 3+5 F minor vii-I AC

b29-43 4+4+3+4 Gb major ii-V HCA: b44-55 4+4+4 F# major ii-V HC

b56-71 4+3+3+6 F# major V-I AC

23

Audio alignment: MATCH

• Audio alignment of exposition (bars 1-19) & recapitulation (bars 44-71) on the six investigated recordings– NB. bars 44-52 in recap

is identical to bars 1-9

• demo

24

Intensity analysis Praat vs. Sonic Visualiser

Brahms, Op.99, ii, b1-19, Casals Brahms, Op.99, ii, Casals

25

Intensity analysis Praat vs. Sonic Visualiser

Praat Sonic Visualiser

Visualisation not efficient setting changeable

Measurement v v

"Relative" level setting changeable currently unavailable

Analysable duration length 3 min 30 sec no limitation

26

Dynamic peaks: Brahms, Op.99, ii, bars 1-19

Cellists Performance hierarchy dynamic measurementCasals F#4: b5 b5-10 C#5: b8 E#5: b15

58.6 dB crescendo 66.9 dB 62.6 dBPiatigorsky F#4: b5 b5-10 C#5: b8 D#5: b10 E#5: b15

51.9 dB crescendo 67.9 dB 66.2 dB 65.4 dBDu Pré F#4: b5 b5-10 C#5: b8 C#5: b10 E#5: b15

50.7 dB crescendo 71.8 dB 69.7 dB 67.6 dBY-Y Ma F#4: b5 b5-10 C#5: b8 D#5: b10 A#5: b15

54.9 dB crescendo 64.24 dB 66.24 dB 65.67 dBRostropovich F#4: b5 b5-10 C#5: b8

58 dB crescendo 72.6 dBMaisky F#4: b5 b5-10 C#5: b8

63.1 dB crescendo 74 dB

27

Tempo Modification: Brahms, Op.99, ii

0

1

2

3

4

5

6

7

CasalsPiatigorskyDu PreY-Y MaRostropovichMaisky

[Exposition] [Development] [Recapitulation]

seco

nd

s

12

20

28

44

48

56 71

bars

performed score duration: data captured through Sonic Visualiser

28

Equations

Pearson’s product-moment correlation

T-test

29

Correlation on timing

x = Casals’ timing, y = Y-Y Ma’s, observations: 142

r = 0.37, t = 4.79, (p = 0.000004)

30

Correlation on timing

Teacher-student timing fluctuations correlation t-test p valueCasals's timing vs. Du Pré's r = 0.6 t = 8.91 p < 0.0000001

Casals's vs. Y-Y Ma's r = 0.37 t = 4.79 p = 0.000004Random relation timing fluctuations correlation t-test p value

Casals's vs. Piatigorsky's r = 0.5 t = 7.51 p < 0.0000001Casals's vs. Rostropovich’s r = 0.4 t = 5.23 p = 0.000001

Casals's vs. Maisky's r = 0.51 t = 7.15 p < 0.0000001

31

IOI: performed score duration Brahms, Op.99, ii, bars 1-19

500

800

1100

1400

1700

2000

Casals

DuPre

Y-YMa

[4+4+3] [4+4]

IOI

ms

5 9 12

16

19bars

performed score duration: data captured through BeatRoot.

32

Correlation on timing

Teacher-student timing fluctuations correlation t-test p valueCasals's timing vs. Du Pré's r = 0.63 t = 10.02 p < 0.0000001

Casals's vs. Y-Y Ma's r = 0.52 t = 7.55 p < 0.0000001Random relation timing fluctuations correlation t-test p value

Casals's vs. Piatigorsky's r = 0.55 t = 7.98 p < 0.0000001Casals's vs. Rostropovich's r = 0.41 t = 5.51 p < 0.0000001

Casals's vs. Maisky's r = 0.14 t = 1.79 p = 0.07

33

Summary/Conclusion

...

… & more?

35

What can be helpful?Goldsmiths Digital Studios

Soundspotter versions 1 (& 2)

sub-space analysis

Repeat structure detecting tool

as shown in the Mazurka case study

C4DM, Queen MaryBeatRoot

intensity level measurement option from the zwicker model

Sonic Visualiser

3 items submitted in the features tracker

Soundbite

Windows version, please!

Thank you!Stephen Cottrell, Tim Crawford, Michael Casey

Goldsmiths College

Simon Dixon C4DM Queen Mary Craig Sapp CHARM

Christophe Rhodes Goldsmiths Chris Cannam C4DM

Aaron Williamon RCM

OMRAS2 teams in Queen Mary & Goldsmiths

& Goldsmiths College, University of London