1. Representation of Musical Information
Donald Byrd
School of Informatics & Jacobs School of Music
Indiana University
Updated 3 Oct. 2007
Copyright © , Donald Byrd

2. Review: Classification: Logician General’s Warning
Classification is dangerous to your understanding
Almost everything in the real world is messy, ill-defined
Absolute correlations between characteristics are rare
Example: some mammals lay eggs; some are “naked”
Example: was the first real piano Cristofori's (ca. 1700), Broadwood's (ca. 1790), or another?
People say “an X has characteristics A, B, C…”
Usually mean “an X has A, & usually B, C…”
Leads to:
People who know better claiming absolute correlations
“Is it this or that or that?” questions that don’t have an answer
Don changing his mind
But lack of classification is also dangerous to your understanding!
3 Oct. 07

3. Basic Representations of Music & Audio (1)
Audio (e.g., CD, MP3): like speech
Time-stamped Events (e.g., MIDI file): like unformatted text
Music Notation: like text with complex formatting
27 Jan.

4. Basic Representations of Music & Audio (2)
Audio Time-stamped Events Music Notation
Common examples CD, MP3 file Standard MIDI File Sheet music
Unit Sample Event Note, clef, lyric, etc.
Explicit structure none little (partial voicing much (complete
information) voicing information)
Avg. rel. storage
Convert to left - easy OK job: easy
Convert to right 1 note: pretty easy OK job: fairly hard -
other: hard or very hard
Ideal for music music music
bird/animal sounds
sound effects
speech
27 Jan.

5. Basic Representations of Music & Audio (3)
Audio: no (explicit) structure
Events/MIDI: simple structure
Notation: very complex structure
2 Oct. 07

6. Dimensions of Music Representations & Encodings (1)
• CMN
(After Wiggins et al (1993). A Framework for the Evaluation of Music Representation Systems.)
rev. 20 Feb. 07

7. Dimensions of Music Representations & Encodings (2)
Expressive completeness
How much of all possible music can the representation express?
Includes synthesized as well as acoustic sounds!
Waveform (=audio) is truly “complete”
Exception, sort of: conceptual music
E.g., Tom Johnson: Celestial Music for Imaginary Trumpets (notes on 100 ledger lines), Cage: 4’ 33” (of silence), etc.
Structural generality
How much of structure in any piece of music can the representation express?
Music notation with repeat signs, etc. still expresses nowhere near all possible structure
rev. 31 Jan. 07

8. Representation vs. Encoding
Representation: what information is conveyed?
More abstract (conceptual)
Basic = general type of info; specific = exact type
Encoding: how is the information conveyed?
More concrete: in computer (“bits”)…or on paper (“atoms”)!
One representation can have many encodings
“Atoms” example: music notation in printed or Braille form
“Bits” example: any kind of text in ASCII vs. Unicode
“Bits” example: formatted text in HTML, RTF, .doc
30 Jan. 06

9. Representation Example: a Bit of Mozart
The first few measures of Variation 8 of the “Twinkle” Variations
27 Jan.

10. In Notation Form: Nightingale Notelist
%%Notelist-V2 file='MozartRepresentationEx' partstaves=2 0 startmeas=193
C stf=1 type=3
C stf=2 type=10
K stf=1 KS=3 b
K stf=2 KS=3 b
T stf=1 num=2 denom=4
T stf=2 num=2 denom=4
A v=1 npt=1 stf=1 S1 'Variation 8'
D stf=1 dType=5
N t=0 v=1 npt=1 stf=1 dur=5 dots=0 nn=72 acc=0 eAcc=3 pDur=228 vel= appear=1
R t=0 v=2 npt=1 stf=2 dur=-1 dots= appear=1
N t=240 v=1 npt=1 stf=1 dur=5 dots=0 nn=74 acc=0 eAcc=3 pDur=228 vel= appear=1
N t=480 v=1 npt=1 stf=1 dur=5 dots=0 nn=75 acc=0 eAcc=2 pDur=228 vel= appear=1
N t=720 v=1 npt=1 stf=1 dur=5 dots=0 nn=77 acc=0 eAcc=3 pDur=228 vel= appear=1
/ t=960 type=1
N t=960 v=1 npt=1 stf=1 dur=4 dots=0 nn=79 acc=0 eAcc=3 pDur=456 vel= appear=1
(etc. File size: 1862 bytes)
27 Jan.

11. An Event Form: Standard MIDI File (file dump)
0: 4D E0 4D54 MThd ‡MT
16: 726B FF B70 C000 FF58 rk......Q..p¿..X
32: FF 2F00 4D54 726B ñ4./.MTrk..
48: FF E 6F U....Piano.êH8Å
64: C90 4A A 400C 904B
80: B40 0C90 4D D 400C
96: 904F F F
112: F D
128: 804D 400D FF2F 004D B
144: FF E6F 8F B Pianoè.êA+ÅdÄ
160: C C
176: C FF2F
192:
5 Feb.

12. An Event Form: Standard MIDI File (interpreted)
Header format=1 ntrks=3 division=480
Track #1 start
t=0 Tempo microsec/MIDI-qtr=749760
t=0 Time sig=2/4 MIDI-clocks/click=24 32nd-notes/24-MIDI-clocks=8
t=2868 Meta event, end of track
Track end
Track #2 start
t=0 Meta Text, type=0x03 (Sequence/Track Name) leng=5
Text = <Piano>
t=0 NOn ch=1 num=72 vel=56
t=228 NOff ch=1 num=72 vel=64
t=240 NOn ch=1 num=74 vel=56
t=468 NOff ch=1 num=74 vel=64
(etc. File size: 193 bytes)
27 Jan.

13. Basic and Specific Representations vs. Encodings
Basic and Specific Representations (above the line)
Encodings (below the line)
rev. 15 Feb.

14. Time Domain & Frequency Domain (1)
Time domain involves waveforms
Frequency domain involves spectra
Fourier’s Theorem
Any periodic signal can be described exactly as the sum of sine waves at integral multiples of its fundamental frequency (Fourier analysis)
Fourier Transform takes time domain to frequency
Inverse Fourier Transform takes freq. domain to time
Fourier synthesis is usual kind of additive synthesis
Definite-pitched sounds are (more-or-less) periodic
8 Feb. 07

15. Time Domain & Frequency Domain (2)
Sine waves in trigonometry
Phase in degrees (0 to 360)
“Simple” example of Fourier synthesis: perfect square wave = an infinite number of odd harmonics
…but only if they’re all in phase
Demo: Fourier applet
31 Jan. 07

16. Time Domain & Frequency Domain (3)
But real-world sounds are almost never periodic!
True, but definite-pitched notes are “close enough” for Fourier analysis to be useful
In reality, usually a series of short-term Fourier Transforms (STFTs)
Look at spectra of individual notes (from Iowa samples, EBU arpeggios)
This is mathematics & physics; perception (psychoacoustics) is different, subtle
We perceive musical sound in both domains—sometimes more one, sometimes more the other
Phase affects waveform, but maybe not perception
25 Feb. 07

17. Time Domain & Frequency Domain (4)
Summary
Periodic waveforms: clearcut definite pitch
With sharp corners: much high-freq. energy
square wave; loud trumpet
Without sharp corners: little high-freq. energy
sine wave; low, soft flute
Almost-periodic waveforms: definite pitch
piano
Aperiodic waveforms: noisy, no definite pitch
cymbal, bass drum
25 Feb. 07

18. Real-World Musical Sounds
The “Attack/Sustain/Release” model for notes
Attack, Sustain, Release modified from recordings
Used in the Kurzweil 250 (1984), etc.
Original version had only 2 MB for all samples
Piano had diff. samples for 2 loudness levels
…and diff. sound for every 4-6 semitones
1-2 sec. per sample for A+S+R
How good did the K250 really sound?
COUNTDOWN, by Christopher Yavelow
“An opera for the nuclear age”
“the ‘orchestral accompaniment’ is in reality a Kurzweil-250 digital sampler, synchronized to the baton of the conductor…”
12 Feb. 07

19. Real-World Musical Sounds
Nowadays, can afford “unlimited” sustain
…but also need diff. sounds for many (8?) diff. loudness levels (multisampling)
“All Together Now”, Electronic Musician, Jan. 2007
…and diff. sound for every semitone or two
W/ unlimited sustain, takes gigabytes just for piano!
19 Feb. 07

20. Scholars (and others) Beware!
Plausible (at the time) assumptions
Stomach ulcers can’t be caused by organisms (20th century)
Men have more teeth than women (ancient)
What you expect & what you see
Sponges, dinosaurs, etc.: discuss later
What you expect & what you hear
Don & the Kurzweil 250 flute sound
Don, a famous musician, & K250 handclaps
Huron on what he “knew” & learned
R. Moog at Kurzweil & piano touch
rev. 20 Sep. 2006

21. Uncompressed Audio Files are Big
1 byte = 8 bits (nearly always)
How much data on a CD?
CD audio is 44,100 samples/channel/sec. * 2 bytes/sample * 2 channels = 176,400 bytes/sec., or 10.5 MByte/min.
CD can store up to 74 min. (or 80) of music
10.5 MByte/min. * 74 min. = 777 MBytes
Actually more: also index, error correction data, etc.
22 Sep. 2006

22. Compressed Audio: Lossless & Lossy
Don’t confuse data compression and dynamic-range compression (a.k.a. audio level compression, limiting)
Codec = COmpressor/DECompressor
Lossless compression
Standard methods (LZW: .zip, etc.) don’t do much for audio
Audio specific methods
MLP used for DVD-Audio
Apple & Microsoft Lossless
Lossy compression
Depends on psychoacoustics (“perceptual coding”)
16 Feb. 06

23. Lossless Compression of Text
Lossless compression of a children’s nursery rhyme
Pease porridge hot,
Pease porridge cold,
Pease porridge in the pot,
Nine days old;
Some like it hot,
Some like it cold,
Some like it in the pot,
Nine days old.
Diagram from Witten, Moffat, & Bell, Managing Gigabytes, 2nd ed.
12 Feb. 07

24. Specs for Some Common Audio Formats
13 Feb. 06

25. Psychoacoustics & Perceptual Coding
Pohlmann, Ken (2005). Principles of Digital Audio, 5th ed., Chapter 10: Perceptual Coding
Rationale: much better data compression
Based on physiology of ear and critical bands
Not fixed frequency: any sound creates one or more critical bands
Masking
Depends on relative loudness & frequency
Noise is much better than pitched sounds
Threshhold of hearing
Depends greatly on frequency
22 Sep. 2006

26. Compressed Audio: Lossy Compression (1)
General method
1. Divide signal into sub-bands by frequency
2. Take advantage of (e.g.):
Masking (“shadows”), via amplitude within critical bands
Threshhold of audibility (varies w/ frequency)
Redundancy among channels
MPEG-1 layers I thru III (MP1, 2, 3), AAC get better & better compression via more & more complex techniques
“There is probably no limit to the complexity of psychoacoustics.” --Pohlmann, 5th ed.
However, there probably is an “asymptotic” limit to compression!
Implemented in hardware or software codecs
22 Feb. 06

27. Compressed Audio: Lossy Compression (2)
Evaluation via critical listening is essential
ITU 5-point scale
5 = imperceptible, 4 = perceptible but not annoying, 3 = slightly annoying, 2 = annoying, 1 = very annoying
Careful tests: often double-blind, triple-stimulus, hidden reference
E.g., ISO qualifying AAC with 31 expert listeners (cf. Hall article)
Test materials chosen to stress codecs
Common useful tests: glockenspiel, castanets, triangle, harpsichord, speech, trumpet
Soulodre’s worst-case tracks: bass clarinet arpeggio, bowed double bass, harpsichord arpeggio, pitch pipe, muted trumpet
References: Pohlmann Principles of Digital Audio (on reserve)
17 Feb. 06

28. Hybrid Representation & Compression (1)
Events (with “predefined” timbre) take very little space
Mozart fragment AIFF (CD-quality audio): 794,166 bytes
Mozart fragment MIDI file: 193 bytes
Timbre takes same amount of space, regardless of music length!
Problem: don’t have exact timbre for any performance
CSound, CMusic, etc. have MIDI-like score and software synthesis def. of orchestra
17 Feb. 07

29. Hybrid Representation & Compression (2)
Mike Hawley’s approach: find structure in audio; create events & timbre definition
Hawley, Michael J. (1990). The Personal Orchestra, or, Audio Data Compression by 10000:1. Usenix Computing Systems Journal 3(2), pp. 289—329.
Could hybrid event/audio representation lead to his “audio data compression by a factor of 10,000”?
Maybe, but no time soon!
17 Feb. 07

30. An Event Form: Standard MIDI File (file dump)
0: 4D E0 4D54 MThd ‡MT
16: 726B FF B70 C000 FF58 rk......Q..p¿..X
32: FF 2F00 4D54 726B ñ4./.MTrk..
48: FF E 6F U....Piano.êH8Å
64: C90 4A A 400C 904B
80: B40 0C90 4D D 400C
96: 904F F F
112: F D
128: 804D 400D FF2F 004D B
144: FF E6F 8F B Pianoè.êA+ÅdÄ
160: C C
176: C FF2F
192:
5 Feb.

31. An Event Form: Standard MIDI File (interpreted)
Header format=1 ntrks=3 division=480
Track #1 start
t=0 Tempo microsec/MIDI-qtr=749760
t=0 Time sig=2/4 MIDI-clocks/click=24 32nd-notes/24-MIDI-clocks=8
t=2868 Meta event, end of track
Track end
Track #2 start
t=0 Meta Text, type=0x03 (Sequence/Track Name) leng=5
Text = <Piano>
t=0 NOn ch=1 num=72 vel=56
t=228 NOff ch=1 num=72 vel=64
t=240 NOn ch=1 num=74 vel=56
t=468 NOff ch=1 num=74 vel=64
(etc. File size: 193 bytes)
27 Jan.

32. MIDI (Musical Instrument Digital Interface) (1)
Invented in early 1980’s
Dawn of personal computers
Designed as simple (& cheap to implement) real-time protocol for communication between synthesizers
Low bandwidth: Kbps
Top bit of byte: 1 = status, 0 = data
Numbers usually 7 bits (range 0-127); sometimes 14 or even 21
Message types
Channel: Channel Voice, Channel Mode
System: System Common, System Real-Time, System Exclusive
5 Feb. 06

33. MIDI (2) Important standard Events are mostly Channel Voice msgs
Note On: channel (1-16), note number (0-127), on velocity
Note Off: channel, note number, off velocity
Can change “voice” (really patch!) any time with Program Change msg
A way around the 16-channel limit: cables
may or may not correspond to a physical cable
each cable supports 16 channels independent of others
Systems with 4 (=64 channels) or 8 cables (=128) are common
MIDI Monitor allows watching MIDI in real time
Freeware and open source!
5 Feb. 06

34. MIDI Sequencers Record, edit, & play SMFs (Standard MIDI Files)
Standard views
Piano roll
often with velocity, controllers, etc., in parallel
Event list
Other: Mixer, “Music notation”, etc.
Standard editing
Adding digital audio
Personal computers & software-development tools have gotten more & more powerful
=> "digital audio sequencers”: audio & MIDI (stored in hybrid encodings)
Making results more musical: “Humanize”
Timing, etc. isn’t mechanical—but not really musical (yet)
8 Feb. 06

35. Is a MIDI File a “Score” or a Performance?
MIDI files are often used to encode music from notation
…but also often used to describe performances!
What’s the difference?
Timing
Dynamics
Realizing ornaments, etc.
For scores, MIDI files are very limited
Max. 16 explicit voices, no spelling info, no slurs, etc.
…though not as badly as many assume
Can include time sig., key sig., text/lyrics, etc.
Cf. “Dimensions of Music Representations & Encodings” graph
15 Feb. 07

36. Another Warning: Terminology (1)
A perilous question: “How many voices does this synthesizer have?”
Syllogism
Careless and incorrect use of technical terms is dangerous to your learning much
Experts very often use technical terms carelessly
Beginners often use technical terms incorrectly
Therefore, your learning much is in danger
Somewhat exaggerated, but only somewhat
5 Feb. 06

37. Another Warning: Terminology (2)
Not-too-serious case: “system”
Confusion because both standard (common) computer term & standard (rare but useful) music term
Serious case: patch, program, timbre, or voice
Vocabulary def.: Patch: referring to event-based systems such as MIDI and most synthesizers (particularly hardware synthesizers), a setting that produces a specific timbre, perhaps with additional features. The terms "voice", "timbre", and "program" are all used for the identical concept; all have the potential to cause substantial confusion and should be avoided as much as possible
“Patch” is the only unambiguous term of the four
…but the official MIDI specification (& almost everything else) talks about “voices” (as in “Channel Voice messages control the instrument’s 16 voices”)
…and to change the “voice”, you use a “program change”!
6 Feb. 06

38. Another Warning: Terminology (3)
Some terminology is just plain difficult
Example: “Representation” vs. “Encoding”
Distinction: 1st is more abstract, 2nd more concrete
…but what does that mean?
Explaining milk to a blind person: “a white liquid...”
Don’s precision involves being very careful with terminology, difficult or not
Vocabulary is important source
Cf. other sources
Contributions are welcome
6 Feb. 06

39. Selfridge-Field on Describing Musical Information
Cf. Selfridge-Field, E. (1997). Describing Musical Information.
What is Music Representation? (informal use of term!)
Codes in Common Use: solfegge (pitch only), CMN, etc.
“Representations” for Computer Application: “total”, MIDI
Parameters of Musical Information
Contexts: sound, notation/graphical, analytic, semantic; gestural?
Concentrates on 1st three
Processing Order: horizontal or vertical priority
Code Categories
Sound Related Codes: MIDI and other
Music Notation Codes: DARMS, SCORE, Notelist, Braille!?, etc.
Musical Data for Analysis: Plaine and Easie, Kern, MuseData, etc.
Representations of Musical Patterns and Process
Interchange Codes: SMDL, NIFF, etc.; almost obsolete!
30 Jan. 06

40. Review: The Four Parameters of Notes
Four basic parameters of a definite-pitched musical note
1. pitch: how high or low the sound is: perceptual analog of frequency
2. duration: how long the note lasts
3. loudness: perceptual analog of amplitude
4. timbre or tone quality
Above is decreasing order of importance for most Western music
…and decreasing order of explicitness in CMN!

41. Review: How to Read Music Without Really Trying
CMN shows at least six aspects of music:
NP1. Pitches (how high or low): on vertical axis
NP2. Durations (how long): indicated by note/rest shapes
NP3. Loudness: indicated by signs like p , mf , etc.
NP4. Timbre (tone quality): indicated with words like “violin”, “pizzicato”, etc.
Start times: on horizontal axis
Voicing: mostly indicated by staff; in complex cases also shown by stem direction, beams, etc.
See “Essentials of Music Reading” musical example.

42. Complex Notation (Selfridge-Field’s Fig. 1-4)
Complications on staff 2:
Editorial additions (small notes)
Instruments sharing notes only some of the time
Mixed durations in double stops
Multiple voices (divisi notation)
Rapidly gets worse with more than 2!
10 Feb.

43. Complex Notation (Selfridge-Field’s Fig. 1-4)
Multiple voices rapidly gets worse with more than 2
2 voices in mm. 5-6: not bad: stem direction is enough
3 voices in m. 7: notes must move sideways
4 voices in m. 8: almost unreadable—without color!
Acceptable because exact voice is rarely important
rev. 12 Feb.

44. Domains of Musical Information
Independent graphic and performance info common
Cadenzas (classical), swing (jazz), rubato passages (all music)
CMN “counterexamples” show importance of independent graphic and logical info
Debussy: bass clef below the staff
Chopin: noteheads are normal 16ths in one voice, triplets in another
Mockingbird (early 1980’s) pioneered three domains:
Logical: “ note is a qtr note” (= ESF(Selfridge-Field)’s “notation”)
Performance: “ note sounds for 456/480ths of a quarter” (= ESF’s “sound”; also called gestural)
Graphic: “ notehead is diamond shaped” (= ESF’s “ notation”)
Nightingale and other programs followed
SMDL added fourth domain
Analytic: for Roman numerals, Schenkerian level, etc. (= ESF’s “analytic”)
1 Feb. 06

45. Representing Voicing in MIDI Files vs. Notation
Music Notation
Explicit via tracks (max. of 16)
Mostly explicit via staves, stem direction, voice-leading lines
Implicit via patch, etc.
Mostly implicit via stem direction, beaming, slurs, alignment, etc.
20 Feb. 07

46. Representing Basic Parameters in MIDI Files Vs. Notation
Music Notation
Timing (incl. duration)
Metric or time-code-based time
Metric: ticks per quarter note (e.g., 480, 1024)
Time-code-based: can be SMPTE or millisec.
Encoding as delta time, to save space
Metric. Relative duration via notehead shape, aug. dots, tuplets, fermatas, etc.; relative time from alignment. Tempo & metronome marks
Pitch
Note number = piano key, plus (global) pitchbend
No distinction between spellings
Spelling with accidentals, including double & (very rarely) triple
Dynamics
Velocity: on (attack) & off (release)
pppp… to ffff…, hairpins, text markings, accents, etc.
Timbre
Patch no., aftertouch, off velocity
Instrument name, text markings, symbols, etc.
20 Feb. 07

47. Communicating about Music
Basic principle of communicating with people: say just what’s necessary
Strunk & White: “Omit needless words”
Applies to a lecture or a notation
22 Feb. 07

48. Representation, from Abstract to Concrete
Cf. Basic Representations of Music & Audio
Abstract: represention: semantics only
Intermediate: syntax (mapping rules)?
Concrete
for use by computers: encoding
for use by humans: if visual, notation (involves graphics and/or typography)
Analogous to knowledge representation vs. data structure

49. Semantics in Music Denotation (explicit, well-defined)...
vs. Connotation (implicit, ill-defined)
In text
Two “definitions” of pig:
1. Ugh! Dirty, evil-smelling creatures, wallowing in filthy sties! (Hayakawa)
2. Mammal with short legs, cloven hoofs, bristly hair, and a cartilaginous snout used for digging (Amer. Heritage)
Prose is “mostly” denotation
Poetry is art => connotation much more important
Music is always art, & only connotation!
What is a musical idea?
Major issue for content-based music IR

50. From Representation to Notation
Choosing a representation inevitably introduces bias
Given a representation, choosing notation inevitably introduces more bias
Important to consider the purpose (R. Davis et al; Wiggins et al)
For huge body of important music, we have no choice: notation is CMN (Conventional Music Notation)!
Really “CWMN” (W = Western)
Alternative for some music: tablature (guitar, lute, etc.)
CMN is among the most successful notations ever...
but enormously complex and subtle

51. Review: How People Find Text Information
What user wants is almost always concepts…
But computer can only recognize words

52. Review: How Computers Find Text Information
“Stemming, stopping, query expansion” are all tricks to increase precision & recall (avoid false negatives & false positives) due to synonyms, variant forms of words, etc.

53. Notation Says Much about Representation
CMN standard for Western music after ca.1650
Evolved for “classical” music, but heavily used for very wide range (pop, jazz, folk, etc.)
Composers/arrangers/transcribers have pushed it hard => reveals things about music representation in general
Will concentrate on notation (CMN)

54. Problems: Example 1 (superficial but interesting)
Ravel work has slur with 7 inflection points
Impressive, but complexity is purely graphical
No big deal in terms of representation
…but influence of performance on notation is revealing

55. Duration and Higher-Level Concepts of Time
Schubert Impromptu (& e 4)
Measures: everything between barlines
Time signature: 3/4 = 3 quarter notes per measure
Triplets: 3 notes in the time normally used by 2
General concept is tuplets

56. Problems: Example 2 (Deep)
Chopin Nocturne has nasty situation (& e 5)
One notehead is triplet in one voice, but normal duration in another
“Semantics” (execution) well-defined, obvious
Note starts 1/16 before barline…
But also (2/3)*(1/16) before barline! How to play?
Reason: musical necessity
Solution for performer: “rubato”
Solution for music IR program: ?

57. Problems: Example 3 (Medium)
Bach: time signature change in middle of measure
(& e 6)
“Semantics” well-defined and obvious
Measure has duration of 18 16ths…
But not until the middle of the measure!
How does this make sense?
Triplets express same relationship as equivalent simple/compound meter
Invisible (unmarked) triplets
Cf. Bach Prelude: two time signatures at once (& e 7)
Reason: avoid clutter

58. Problem 4 (Medium) Brahms Capriccio (& e 8)
Time signature 6/8 => measure lasts 12 16ths
A dotted half note always lasts 12 16ths…
but here it clearly lasts only 11 16ths!
Reason: avoid clutter

59. Two Ways to Have Two Clefs at Once
Clef gives vertical offset to determine pitch
Debussy (& e 9)
Bizarrely obvious something odd involving clefs
Ravel (& e 10)
Only comparing time signature (3/8) and note durations makes it clear both clefs affect whole measure
Reason: save space (by avoiding a 3rd staff)

60. Surprise: Music Notation has Meta-Principles! (1)
1. Maximize readability (intelligibility)
Avoid clutter = “Omit Needless Symbols”
Try to assume just the right things for audience
Audience for CMN is (primarily) performers
General principle of any communication
Applies to talks as well as music notation!
Examples: Schubert, Bach, Brahms

61. Surprise: Music Notation has Meta-Principles! (2)
2. Minimize space used
Save space => fewer page turns (helps performer); also cheaper to print (helps publisher)
Squeezing much music into little space is a major factor in complexity of CMN
Especially important for music: real-time, hands full
Examples: Telemann, Debussy, Ravel

62. The “Rules” of Music Notation
Tempting to assume that rules of such an elaborate & successful system as CMN work (self-consistent, reasonably unambiguous, etc.) in every case
But (a) “rules” evolved, with no established authority; (b) many of the “rules” are very nebulous
In common cases, there's no problem
If you try to make every rule as precise as possible, result is certainly not self-consistent
Trying to save space makes rules interact; something has to give!

63. Music Notation Software and Intelligence
Despite odd notation, really nothing strange going on in almost all of these examples
Ravel slur, Debussy & Ravel 2 simultaneous clefs, Bach & Schubert invisible triplets, Brahms “short” dotted-half note, Telemann 4 voices/staff are all simple situations
Chopin Nocturne is complex
Programmers try to help users by having programs do things “automatically”
A good idea if software knows enough to do the right thing “almost all” the time—but no program does!
Notation programs convert CMN to performance (MIDI) and vice-versa => requires shallow “semantics”; makes things much harder

64. Conclusions: Review (1)
Representations express Semantics
Semantics of Music; Denotation & Connotation
Principles of CMN
Meta-Principles of CMN
1. Maximize readability; Omit Needless Symbols
Try to assume just the right things for audience
General principle of any communication
2. Minimize space used
Save space => fewer page turns, less paper

65. Conclusions: Review (2)
We need CMN or equivalent to solve spectrum of music-IR (and other music-IT) problems
But CMN can’t represent everything we want
Even when it can, actually may not (esp. explicitly)
Need high-level intelligence to interpret
Solution: unknown
Likely to require major funding :-)

66. Why Music-IR Research is Important (Outside of Music)
Some problems directly related to other areas of informatics
Example: Approximate string matching in bioinformatics
Encourages progress on real semantics
Connotation is an important part of meaning in everything
Can often ignore, but any semantics in arts forces you to deal with connotation
Music is at least as quantifiable as any art, so likely to be more tractable than others!

67. Different Classifications of Music Encodings
10 Feb.

68. Mozart: Variations for piano, K. 265, on “Ah, vous dirais-je, Maman”, a.k.a. Twinkle