1. Music Notation and Encoding ISMIR Graduate School, Barcelona 2004 Musicology 2 Frans Wiering, ICS, Utrecht University

2. Outline Common Music Notation Other notations Encoding music notation Music corpora

3. How to Read Music Without Really Trying Donald Byrd, School of Music, Indiana University Rev. August 2004 (adapted FW) 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

4. CMN (Common/Conventional Music Notation) (after Don Byrd) 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. Also shown:  measures (beat groups)  pitch modifiers (flats and sharps)

5. The keyboard (www.keyscreen.com)

6. Durations (American English) (library.thinkquest.org) 1. Whole 2. Half 3. Quarter 4. Eighth 5. Sixteenth 6. etc. duration tricks:rests:

7. Rhythm and meter rhythm: the pattern of (relative) durations measure: recurring beat pattern meter: type of measure, expressed in fraction  number of beats  type of beat  eg: 4/4, 7/2

8. Extensibility of notation (representational completeness) CMN seems closed system (like an alphabet), but it isn’t  historical change  creativity  precision (Mahler scores!)  abbreviation  example: chord abbreviations, as in fake books http://www.eddielandsberg.com/ jeanine_800x965.jpg http://www.eddielandsberg.com/ jeanine_800x965.jpg Extremes of CMN collected by Don Byrd at http://php.indiana.edu/~donbyrd/ CMNExtremes.htm http://php.indiana.edu/~donbyrd/ CMNExtremes.htm

9. Before CMN (1) neumes, c. 900 (from New Grove) plainchant, 17 th c. (www.loc.gov) describe differences to CMN a little

10. Before CMN (2) mensural notation, early 16 th c. (www.loc.gov) (from DIAMM)

11. Tablature (exx: New Grove) 18 th c. lute tablature koto tablature klavarskribo (20 th c.)

12. Different aspects of notation mnemonic  neumes framework for improvisation  fake book  basso continuo  John Cage performer instruction  (lute) tablature  much CMN composer’s intention  19th/20th century classical music

13. What is a basic musical ‘unit’ example units  song the performance/recording  classical music work: independent creation, realized in (more than one) performance and codified in notation  early music instance: piece exists in many different forms  (but see also Chopin first editions!) number of voices, levels of elaboration etc. Opera  improvisation framework: archetypal melody, chord pattern, ‘raga’ consequences:  the work ‘is’ not always the notation  not all aspects of notation are always equally important (may reflect local circumstances rather than intention)  it may be necessary to derive supplementary information but how?

14. Summary principal dimensions: pitch and time additional dimensions complexity non-CM notations relation to basic musical unit

15. Encoding music notation General characteristics Purposes of music encoding before MIR Surveys Requirements Some (once) popular encoding systems Unification and interchange Music corpora What if you want to work with encoded notation

16. General characteristics translating notation to digital representation  or note-like information, as in MIDI alphanumeric and binary encoding systems very many formats have been proposed and are still in use interchange is a problem generally not designed for retrieval

17. Purposes of music encoding before MIR Music printing and publishing  DARMS project and encoding (60s-80s)  proprietary systems since the 80s (Finale, Sibelius)  encoding systems tend to be exhaustive Cataloguing  RISM project: cataloguing all music written before 1800. Beginnings in Plaine and easie code  non-exhaustive systems Music analysis  ESAC: folksong collections  Humdrum: flexible encoding system for arbitrary (combination of) parameters Sound Control  MIDI  playing instructions

18. Surveys www.music-notation.info  Gerd Castan  71 different music encoding systems listed with links to software and web pages  definitely incomplete Beyond MIDI. The Handbook of Musical Codes. ed. Eleanor Selfridge-Field (1997)  c. 27 music encoding systems, detailed descriptions  non-proprietory formats only

19. Requirements some ideas  representational completeness  aptness  extensibility  efficient  structure follows music  mnemonic More on creating encoding systems:  David Huron (1992)  Beyond Midi (chapter by David Halperin)

20. Some (once) popular encoding systems Plaine And Easie Code  Barry S. Brook, Murray Gould (1964)  writing music with a typewriter  (card) catalogues DARMS  Stefan Bauer-Mengelberg (1963); Raymond Erickson  cheap printing of avant-garde music, very rich encoding  set standard for later encoding systems  later: analytical applications [right:] ''4CC/GG/AA/GG/FF/EE/D8.D6E/2C://: [left: ],4C'C/EC/FC/EC/D,B/'C,A/FG/2C://: !I1 !G,!F !M2:4 6Q 6Q / 10Q 10Q / 11Q 11Q / 10Q 10Q / 9Q 9Q / 8Q 8Q / 7Q 7E. 8S / 6H :/: !-50 K next line employs the 'carry feature' for durations $ !M2:4 4Q 11 / 13 11 / 14 11 / 13 11 / 12 10 / 11 9 / 7 8 / 4H :/:

21. More ESAC  Helmut Schaffrath (1980s)  analysis of folksong  monophonic; phrase structure encoded. Used for testing automatic segmenting (Jane Singer?) MuseData  Walter Hewlett, late 1980s  logical content of musical scores  printing, analysis, generation of sound right: KEY[C0000 16 C2/4] MEL[1__1__ 5__5__ 6__6__ 5__5__ 4__4__ 3__3__ 2__2_.3 1___] left: KEY[C0000 4 C2/4] MEL[--1-1 -3-1 -4-1 -3-1 -2--7 -1--6 --4--5 --1_] $ Q:4 K:0 T:2/4 S:2 C1:4 C2:22 S C5 4 1 q d1 back 8 C3 4 2 q u2 C4 4 2 q d2 measure 2 G5 4 1 q d1 back 8 E4 4 2 q d2 C4 4 2 q d2

22. Humdrum David Huron, c. 1990 http://csml.som.ohio-state.edu/Humdrum/ syntax for developing (light-weight) encoding systems  data organised in parallel ‘spines’  humdrum representation for CMN: kern humdrum tools (UNIX) sample analytical problems (total: 350):  Locate instances of the pitch sequence D-S-C-H in Shostakovich's music. Locate instances of the pitch sequence D-S-C-H in Shostakovich's music.  Are German drinking songs more likely to be in triple meter. Are German drinking songs more likely to be in triple meter.  Determine whether Haydn tends to avoid V-IV progressions. Determine whether Haydn tends to avoid V-IV progressions.  Find all woodwind quintets in compound meters that contain a change of key. Find all woodwind quintets in compound meters that contain a change of key.  Classify cadences as either authentic, plagal or deceptive. Classify cadences as either authentic, plagal or deceptive.  Determine whether the words `high,' `hoch,' or `haut' tend to coincide with higher pitches in a vocal work. Determine whether the words `high,' `hoch,' or `haut' tend to coincide with higher pitches in a vocal work. many can be rephrased as music IR problems **kern *staff2*staff1=1- *clefF4*clefG2*k[]*M2/4 *^* 2ryy4C4cc.4c4cc =2=2=2 2ryy4e4gg.4c4gg =3=3=3 2ryy4f4aa.4c4aa =4=4=4 2ryy4e4gg.4c4gg =5=5=5 2ryy4d4ff.4B4ff =6=6=6 2ryy4c4ee.4A4ee =7=7=7

23. MIDI controlling electronic instruments  pitch represented by key number different types of events  note on/off, and many others used for (very lossy) data exchange Not mentioned  Guido  Score MFile 1 2 1024 MTrk 0 TimeSig 2/4 24 8 0 KeySig 0 major 0 Tempo 500000 16385 Meta TrkEnd TrkEnd MTrk 0 Meta TrkName "Acoustic Grand Piano" 0 PrCh ch=1 p=0 0 On ch=1 n=72 v=64 0 On ch=1 n=48 v=64 1024 Off ch=1 n=72 v=0 1024 On ch=1 n=72 v=64 1024 Off ch=1 n=48 v=0 1024 On ch=1 n=60 v=64 2048 Off ch=1 n=72 v=0 2048 Off ch=1 n=60 v=0 2048 On ch=1 n=79 v=64 2048 On ch=1 n=64 v=64 3072 Off ch=1 n=79 v=0 3072 On ch=1 n=79 v=64 3072 Off ch=1 n=64 v=0 3072 On ch=1 n=60 v=64 4096 Off ch=1 n=79 v=0

24. Unification and interchange formats designed for interchange between applications  rich descriptions of musical content SMDL  ISO 10743. Based on HyTime/SGML  domains logical (cantus, the abstract work); markup defined visual (score); container gestural (performance); container analytical; container  never used; concepts circulate NIFF  interchange of music notation data, page-oriented  was supported by part of the industry  still alive???

25. MusicXML developed by Michael Good (www.recordare.com) quotes from the site:  open format, usable by as many applications as possible  Dolet software uses MusicXML to provide a "universal translator" between music notation programs.

26. Music corpora (1) RISM (PAEC)  470.000 incipits on CD-ROM MIDI (everywhere)  http://www.classicalarchives.com/ (8000 works)  restricted access  uneven quality CCARH  http://www.ccarh.org  Musedata, Humdrum (nearly 4000 movements of classical works) sources: out-of copyricht editions of Great Classical Composers quality of encoding

27. Music corpora (2) Graphic formats  Choral Public Domain Library (http://www.cpdl.org/) 7800 scores  sheet music, mostly in graphic formats Esac  http://www.esac-data.org/  20.000 songs and instrumental melodies, mostly from Germany, Poland and China, with minor collections from other (mostly European) countries least incomplete survey:  http://php.indiana.edu/~donbyrd/MusicTestCollections.HTML what happens to music corpora after a while  sad case of Princeton Josquin project (1970s) most punchcards are lost what remains is unusable

28. What if you want to work with encoded notation developing yet another encoding system may not be very wise  most encoding systems are richer than necessary for most music IR applications get scores (as ‘ground truth’ for signal processing?)  collect from the Internet  conversion  not too many  quality issue encode scores  extremely timeconsuming OCR  gets better and better work of Ichiro Fujinaga (will be at ISMIR)  still relatively high error percentages