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![Page 1: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/1.jpg)
The Mazurka Project
Science and Music Seminar
University of Cambridge28 Nov 2006
Craig Stuart SappCentre for the History and Analysis of Recorded MusicRoyal Holloway, University of London
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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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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.
![Page 4: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/4.jpg)
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:
![Page 5: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/5.jpg)
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
primarily due to constant changes in tempo
![Page 6: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/6.jpg)
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
• Beat times are used to create a simulated performance from the score.
1912 4r 4ee=1 =1 =12558 4r 8.ff3021 . 16ee3175 4A 4d 4f 4dd3778 4A 4d 4f 4ff=2 =2 =24430 4r 2ff4914 4A 4c 4f .5541 4A 4c 4e 4ee=3 =3 =36289 4r 24dd6375 . 24ee6461 . 24dd6547 . 8cc#6805 4E 4G# 4d 8dd7012 . 8dd#7219 4E 4G# 4d 8ee7516 . 8b=4 =4 =4
beattimes
lefthand
righthand
interpolated off-beat times
• Score data is in the Humdrum format: http://humdrum.org
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Automatic feature extraction (2)
1912 4r 4ee=1 =1 =12558 4r 8.ff3021 . 16ee3175 4A 4d 4f 4dd3778 4A 4d 4f 4ff=2 =2 =2
• Data is translated to a Matlab-friendly format.
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
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Dynamics & Phrasing
1
2
3
all at once:
rubato
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Tempo graphs
![Page 11: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/11.jpg)
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
![Page 13: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/13.jpg)
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.
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Overall performance correlations
BiretBrailowsky
ChiuFriereIndjic
LuisadaRubinstein 1938Rubinstein 1966
SmithUninsky
Bi LuBr Ch Fl In R8 R6 Sm Un
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Correlation network
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Correlation tree
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Correlation tree (2)
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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)
![Page 22: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/22.jpg)
Same performer (2)2 performances by Horowitz of mazurka 17/4 in A minor plus Biret 1990 performance.
(30 performances compared)
![Page 23: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/23.jpg)
Correlation to average
![Page 24: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/24.jpg)
Individual interpretations
• Idiosyncratic performances which are not emulated by other performers.
(or I don’t have performances that influenced them or they influence)
![Page 25: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/25.jpg)
Possible influences
![Page 26: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/26.jpg)
Student/Teacher
• Francois and Biret both studied with Cortot,
Mazurka in F major 68/3
(20 performances compared)
![Page 27: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/27.jpg)
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
![Page 28: The Mazurka Project Science and Music Seminar University of Cambridge 28 Nov 2006 Craig Stuart Sapp Centre for the History and Analysis of Recorded Music.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bfc81a28abf838ca89d7/html5/thumbnails/28.jpg)
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/
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Extra Slides
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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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Reverse Conducting• Orange = individual taps (multiple sessions) which create bands of time about 100 ms wide.
• Red = average time of individual taps for a particular beat
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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)
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Input to Andrew’s System
Scan the score
Convert to symbolicdata with SharpEye
http://www.visiv.co.uk
Tap to the beats inSonic Visualiser
Convert to Humdrum
data format
Create approximateperformance
scorehttp://www.humdrum.org
http://www.sonicvisualiser.org
Simplifyfor processing
in Matlab