1. 1 Representation of Musical Information Donald Byrd School of Music Indiana University Updated 20 Feb. 2008 Copyright © 2003-08, Donald Byrd

2. 31 Jan. 07 2 Classification: Logician General’s Warning Classification is dangerous to your understanding –Almost everything in the real world is messy –Absolute correlations between characteristics are rare –Example: some mammals lay eggs; some are “naked” –Example: is the piano a keyboard, a string, or a percussion instrument? 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 –Arguments among experts over which characteristic is most fundamental –Don changing his mind

3. rev. 20 Feb. 07 3 Dimensions of Music Representations & Encodings (1) (After Wiggins et al (1993). A Framework for the Evaluation of Music Representation Systems.) CMN

4. rev. 31 Jan. 07 4 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

5. 30 Jan. 06 5 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

6. 27 Jan. 6 Basic Representations of Music & Audio Audio (e.g., CD, MP3): like speech Time-stamped Events (e.g., MIDI file): like unformatted text Music Notation: like text with complex formatting

7. 27 Jan. 7 Basic Representations of Music & Audio AudioTime-stamped EventsMusic Notation Common examplesCD, MP3 fileStandard MIDI FileSheet music UnitSample Event Note, clef, lyric, etc. Explicit structurenone little (partial voicing much (complete information) voicing information) Avg. rel. storage2000 110 Convert to left- easyOK job: easy Convert to right1 note: pretty easyOK job: fairly hard- other: hard or very hard Ideal formusic musicmusic bird/animal sounds sound effects speech

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

9. 27 Jan. 9 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=55...... appear=1 R t=0 v=2 npt=1 stf=2 dur=-1 dots=0...... appear=1 N t=240 v=1 npt=1 stf=1 dur=5 dots=0 nn=74 acc=0 eAcc=3 pDur=228 vel=55...... appear=1 N t=480 v=1 npt=1 stf=1 dur=5 dots=0 nn=75 acc=0 eAcc=2 pDur=228 vel=55...... appear=1 N t=720 v=1 npt=1 stf=1 dur=5 dots=0 nn=77 acc=0 eAcc=3 pDur=228 vel=55...... 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=55...... appear=1 (etc. File size: 1862 bytes)

10. 5 Feb. 10 An Event Form: Standard MIDI File (file dump) 0: 4D54 6864 0000 0006 0001 0003 01E0 4D54 MThd.........‡MT 16: 726B 0000 0014 00FF 5103 0B70 C000 FF58 rk......Q..p¿..X 32: 0402 0218 0896 34FF 2F00 4D54 726B 0000.....ñ4./.MTrk.. 48: 0055 00FF 0305 5069 616E 6F00 9048 3881.U....Piano.êH8Å 64: 6480 4840 0C90 4A38 8164 804A 400C 904B dÄH@.êJ8ÅdÄJ@.êK 80: 3881 6480 4B40 0C90 4D38 8164 804D 400C 8ÅdÄK@.êM8ÅdÄM@. 96: 904F 3883 4880 4F40 1890 4F38 8360 9050 êO8ÉHÄO@.êO8É`êP 112: 3883 4880 4F40 1890 4D38 8330 8050 4018 8ÉHÄO@.êM8É0ÄP@. 128: 804D 400D FF2F 004D 5472 6B00 0000 3200 ÄM@../.MTrk...2. 144: FF03 0550 6961 6E6F 8F00 9041 2B81 6480...Pianoè.êA+ÅdÄ 160: 4140 0C90 4330 8164 8043 400C 9044 3181 A@.êC0ÅdÄC@.êD1Å 176: 6480 4440 0C90 4647 8164 8046 4001 FF2F dÄD@.êFGÅdÄF@../ 192: 00.

11. 27 Jan. 11 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 = 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)

12. 5 Feb. 06 12 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: 31.25 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

13. 5 Feb. 06 13 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!

14. 8 Feb. 06 14 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)

15. 15 Feb. 07 15 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

16. 5 Feb. 06 16 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 often use technical terms carelessly –Beginners often use technical terms incorrectly –Therefore, your learning much is in danger Somewhat exaggerated, but only somewhat

17. 6 Feb. 06 17 Another Warning: Terminology (2) Not-too-serious case: “system” –Confusion because both standard (common) computer term & standard (not common 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”!

18. 6 Feb. 06 18 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

19. 27 Jan. 19 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 = 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)

20. rev. 15 Feb. 20 Basic and Specific Representations vs. Encodings Basic and Specific Representations (above the line) Encodings (below the line)

21. rev. 20 Feb. 08 21 Rudiments of Musical Acoustics Need some musical acoustics for almost anything in digital audio Acoustics: branch of physics that studies sound (of any kind) –Concepts like frequency & amplitude –Besides music, e.g., ultrasonic (for medicine, underwater, etc.) Psychoacoustics: study of how sound is perceived; mostly psychology –Concepts like pitch, loudness, timbre Relationship to physical concepts often roughly logarithmic …but only roughly: always more complex than that

22. 10 Sept. 2006 22 Materials for Studying Audio & Acoustics Musical instrument samples –What are interesting sounds really like? Sine waves, etc. are boring! (cf. addsynenv) Sounds of acoustic instruments are “rich” Vary in every way: with pitch, loudness, time Audacity audio editor –For Windows, Mac OS 9 and X, Linux –Download from http://audacity.sourceforge.net/http://audacity.sourceforge.net/ Sonic Visualiser is promising new alternative Programs in (e.g.) R Fourier applet addsynenv

23. rev. 18 Feb. 08 23 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 sinusoids 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 –Other possibilities: wavelets, Walsh functions Definite-pitched sounds are (more-or-less) periodic

24. 31 Jan. 07 24 Time Domain & Frequency Domain (2) Sine & cosine in trigonometry Phase in degrees (0 to 360, for ordinary use)… Or radians 0 to 2*pi, for technical use) Phase rarely important for us => say sinusoid “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 –http://www.falstad.com/fourier/

25. 25 Feb. 07 25 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 use a series of short-term Fourier Transforms (STFTs): time-frequency domain –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

26. 25 Feb. 07 26 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

27. 12 Feb. 07 27 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…” –http://www.yavelow.com/docs/countdown.html

28. 19 Feb. 07 28 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!

29. rev. 20 Sep. 2006 29 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

30. 22 Sep. 2006 30 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.

31. 16 Feb. 06 31 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”)

32. 12 Feb. 07 32 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.

33. 13 Feb. 06 33 Specs for Some Common Audio Formats

34. 22 Sep. 2006 34 Psychoacoustics & Perceptual Coding Pohlmann, Ken (2005). Principles of Digital Audio, 5 th 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

35. 22 Feb. 06 35 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

36. 17 Feb. 06 36 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)

37. 17 Feb. 07 37 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

38. 17 Feb. 07 38 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!

39. 30 Jan. 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!

40. 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. 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. 10 Feb. 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!

43. rev. 12 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

44. 1 Feb. 06 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”)

45. 20 Feb. 07 45 Representing Voicing in MIDI Files vs. Notation MIDI FileMusic 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.

46. 20 Feb. 07 46 Representing Basic Parameters in MIDI Files Vs. Notation MIDI FileMusic 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. TimbrePatch no., aftertouch, off velocity Instrument name, text markings, symbols, etc.

47. 22 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

48. 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. 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. 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. 51 Review: How People Find Text Information What user wants is almost always concepts… But computer can only recognize words

52. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 10 Feb. 67 Different Classifications of Music Encodings

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