charm advisory board meeting institute for historical research, school of advanced study, ul senate...
TRANSCRIPT
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CHARM Advisory Board Meeting
Institute for Historical Research,School of Advanced Study,
UL Senate House, London
12 Dec 2006
Craig Stuart SappCentre for the History and Analysis of Recorded MusicRoyal Holloway, University of London
The Mazurka Project Overview
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Some facets of music
Composition Performance Listen
Composer Performer Audience
Music Theory Cognitive Psychology
?
fields of generation
fields of analysis
Instrument Maker
Instrument
Acoustics
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Source Material: Mazurka Recordings
29 performances:
• 1,374 recordings of 49 mazurkas
• 65 performers, 73 CDs
= 28 performances/mazurka on average
number of mazurka performancesin each decade
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Performance data extraction
Reverse conducting
Align taps to beats
Automatic feature extraction
tempo by beat
off-beat timings
individualnote timings
individual note loudnesses
• Listen to recording and tap to beats.• Tap times recorded in Sonic Visualiser by tapping on computer keyboard.
• Reverse conducting is real-time response of listener, not actions of performer.• Adjust tap times to correct beat locations.• A bit fuzzy when RH/LH do not play in sync, or for tied notes.
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Reverse conducting• Mazurka project using an audio editor called Sonic Visualiser (SV): http://sonicvisualiser.org
• In SV, you can mark points in time while the audio is playing:
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Beat alignment• Taps from reverse conducting are not exactly aligned with the performance.
• How to adjust to actual note attacks?
• Can be difficult to do by eye in audio editor.
• Very time-consuming to do by ear.
• Solution: audio markup plugins in SV to help locate note attacks:
such as: http://sv.mazurka.org.uk/MzAttack and http://sv.mazurka.org.uk/PowerCurve
primarily due to constant changes in tempo
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Beat alignment (2)• With visual aid of markup, correction becomes easy to do by eye:
= tapped times
= aligned to beats
Example:
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Automatic feature extraction
1912 4r 4ee=1 =1 =12558 4r 8.ff3021 . 16ee3175 4A 4d 4f 4dd3778 4A 4d 4f 4ff=2 =2 =2
• Tapped beats linked score: note
ons
et
nota
ted
dura
tion
pitc
h (M
IDI)
met
ric le
vel
mea
sure
absb
eat
hand
• Automatic alignment and extraction of note onsets and loudnesses with program being developed by Andrew Earis.
1912 646 76 1 0 0 22558 463 77 0 1 1 23021 154 76 -1 1 1.75 23175 603 57 0 1 2 13175 603 62 0 1 2 13175 603 65 0 1 2 13175 603 74 0 1 2 23778 652 57 1 1 3 13778 652 62 1 1 3 13778 652 65 1 1 3 13778 652 77 1 1 3 2
• Estimate times of notes in recording
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MIDI Performance Reconstructions
MIDI file imported as a note layer in Sonic Visualiser:
• Superimposed on spectrogram
• Easy to distinguish pitch/harmonics
• Legato; LH/RH time offsets
“straight” performance matching performers tempobeat-by-beat:
tempo = avg. of performance(pause at beginning)
original recording
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Dynamics & Phrasing
1
2
3
all at once:
rubato
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Average tempo over time• Performances of mazurkas slowing down over time:
Friedman 1930
Rubinstein 1966 Indjic 2001
• Slowing down at about 3 BPM/decade
Laurence Picken, 1967: “Centeral Asian tunes in the Gagaku tradition” in Festschriftfür Walter Wiora. Kassel: Bärenreiter, 545-51.
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Average Tempo over time (2)
• The slow-down in performance tempos is unrelated to the age of the performer
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Tempo graphs
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Mazurka Meter
• Stereotypical mazurka rhythm:
• First beat short• Second beat long
Mazurka in A minorOp. 17, No. 4
measure with longer second beat
measure with longer first beat
• blurred image to show overall structure
B AA A D(A) C
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Timescapes
• Examine the internal tempo structure of a performances
• Plot average tempos over various time-spans in the piece
• Example of a piece with 6 beats at tempos A, B, C, D, E, and F:
average tempo forentire piece
plot of individualtempos
average tempo of adjacent neighbors
3-neighbor average
4-neighbor average
5-neighbor average
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Timescapes (2)average tempo of performance
averagefor performance
slower
faster
phrases
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Comparison of performers
6
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Same performer
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Correlation
Pearson correlation:
• Measures how well two shapes match:
r = 1.0 is an exact match.r = 0.0 means no relation at all.
• What does correlation “mean”?• What does it mean “musically”?
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Overall performance correlations
BiretBrailowsky
ChiuFriereIndjic
LuisadaRubinstein 1938Rubinstein 1966
SmithUninsky
Bi LuBr Ch Fl In R8 R6 Sm Un
Highest correlationto Biret 1990
Lowest correlationto Biret 1990
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Correlation tree• Who is closest to whom?
(with respect to beat temposof an entire performance).
Mazurka in A minor, 68/3
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Correlation tree (2)Mazurka in A minor, 17/4
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Correlation network• How close is everyone to everyone else?
Mazurka in A minor, 17/4
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Correlation scapes• Who is most similar to a particular performer at any given region in the music?
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Same performer over time3 performances by Rubinstein of mazurka 17/4 in A minor
(30 performances compared)
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Same performer (2)2 performances by Horowitz of mazurka 17/4 in A minor plus Biret 1990 performance.
(30 performances compared)
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Student/Teacher
• Francois and Biret both studied with Cortot,
Mazurka in F major 68/3
(20 performances compared)
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Correlation to average
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Possible influences
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Same source recordingThe same performance by Magaloff on two different CD releases
Philips 456 898-2 Philips 426 817/29-2
• Structures at bottoms due to errors in beat extraction or interpreted beat locations (no notes on the beat).
mazurka 17/4 in A minor
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Individual interpretations
• Idiosyncratic performances which are not emulated by other performers.
(or I don’t have performances that influenced them or they influence)
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Purely coincidentalTwo difference performances from two different performers on two different record labels from two different countries.
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For further information
http://mazurka.org.uk
http://www.charm.rhul.ac.uk/