2. 1 Organization of and Searching in Musical Information Donald Byrd School of Music Indiana University 19 January 2006

3. rev. Jan. 20062 Overview 1. Introduction and Motivation 2. Basic Representations 3. Why is Musical Information Hard to Handle? 4. Music vs. Text and Other Media 5. OMRAS and Other Projects 6. Summary

4. 3 1. Introduction and Motivation Three basic forms (representations) of music are important –Audio: most important for most people (general public) All Music Guide (www.allmusicguide.com) has info on >>230,000 CD’s –MIDI files: often best or essential for some musicians, especially for pop, rock, film/TV Hundreds of thousands of MIDI files on the Web –CMN (Conventional Music Notation): often best, sometimes essential for musicians (even amateurs) and music researchers Music holdings of Library of Congress: over 10M items –Includes over 6M pieces of sheet music and tens/hundreds of thousands of scores of operas, symphonies, etc.: all notation, especially Conventional Music Notation (CMN) Differences among the forms are profound

5. 4 2. 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

6. rev. Jan. 20065 Basic Representations of Music & Audio AudioTime-stamped EventsMusic Notation Common examplesCD, MP3 fileStandard MIDI FileSheet music UnitSampleEvent Note, clef, lyric, etc. Explicit structurenonelittle (partial voicing much (complete information) voicing information) Avg. rel. storage2000110 Convert to left- OK job: easyOK job: easyGood job: hard Convert to right1 note: pretty easyOK job: hard- other: hard or very hard Ideal formusic musicmusic bird/animal sounds sound effects speech

7. 6 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!

8. 7 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.

9. 8 3. Why is Musical Information Hard to Handle? 1. Units of meaning: not clear anything in music is analogous to words (all representations) 2.Polyphony: “parallel” independent voices, something like characters in a play (all representations) 3.Recognizing notes (audio only) 4.Other reasons –Musician-friendly I/O is difficult –Diversity: of styles of music, of people interested in music

10. rev. Jan. 20069 Units of Meaning (Problem 1) Not clear anything in music is analogous to words –No explicit delimiters (like Chinese) –Experts don’t agree on “word” boundaries (unlike Chinese) –Music is always art => “meaning” much more subtle! Are notes like words? No. Relative, not absolute, pitch is important Are pitch intervals like words? No. They’re too low level: more like characters Are pitch-interval sequences like words? In some ways, but –Ignores note durations –Ignores relationships between voices (harmony) –Probably little correlation with semantics

11. 10 Independent Voices in Music (Problem 2) J.S. Bach: “St. Anne” Fugue, beginning

12. 11 Independent Voices in Text MARLENE. What I fancy is a rare steak. Gret? ISABELLA. I am of course a member of the / Church of England.* GRET. Potatoes. MARLENE. *I haven’t been to church for years. / I like Christmas carols. ISABELLA. Good works matter more than church attendance. --Caryl Churchill: “Top Girls” (1982), Act 1, Scene 1 M: What I fancy is a rare steak. Gret? I haven’t been... I: I am of course a member of the Church of England. G:Potatoes. Performance (time goes from left to right):

13. 12 Music Notation vs. Audio Relationship between notation and its sound is very subtle Not at all one symbol one symbol –Notes w/ornaments (trills, etc.) are one => many –All symbols but notes are one => zero! –Bach F-major Toccata example Style-dependent –Swing (jazz), dotting (baroque art music) –Improvisation (baroque art music, jazz) –“Events” (20th-century art music) –How well-defined is style-dependent Interpretation is difficult even for musicians –Can take 50-90% of lesson time for performance students

14. 13 Music Perception and Music IR Salience is affected by texture, loudness, etc. –Inner voices in orchestral music rarely salient Streaming effects and cross-voice matching –produced by timbre: Wessel’s illusion (Ex. 1, 2) –produced by register: Telemann example (Ex. 3) Octave identities, timbre and texture –Beethoven “Hammerklavier” Sonata example (Ex.4, 5) –Affects pitch-interval matching

15. 14 4. Music vs. Text and Other Media ———— Explicit Structure ————Salience leastmediummostincreasers Musicaudioeventsnotationloud; thin texture Textaudio (speech)ordinarytext with markup“headlining”: large, written textbold, etc. Imagesphoto, bitmapPostScriptdrawing-programbright color file VideovideotapeMPEG?Premiere filemotion, etc. w/o sound Biological DNA sequences,MEDLINE abstracts?? data3D protein structures

16. 15 Features of Music: Text Analogies Simultaneous independent voices and texture Analogy in text: characters in a play Chords within a voice Analogy in text: character in a play writing something visible to the audience while saying different out loud Rhythm Analogy in text: rhythm in poetry Notes and intervals Note pitches rarely important Intervals more significant, but still very low-level Analogy in text: interval = (very roughly!) letter, not word

17. 16 Features of Text: Music Analogies Words Analogy in music: for practical purposes, none Sentences Analogy in music: phrases (but much less explicit) Paragraphs Analogy in music: sections of a movement (but less explicit) Chapters Analogy in music: movements

18. rev. Jan. 200617 5. OMRAS and its Research OMRAS: Online Music Recognition and Searching –Details at www.omras.org Support from Digital Libraries Initiative, Phase 2 –First major grant for music IR; from 1999 to 2002 Joint project of IU, UMass, and Kings College London Goal: search realistic databases in all three representations Research Tools –True polyphonic search, i.e., search polyphonic music for polyphonic pattern –Full GUI for complex music notation –Modular architecture: to let users mix and match

19. 18 OMRAS Audio-degraded Music IR Experiment Before (original audio recording) After (audio -> MIDI -> audio) Started with recording of 24 preludes and fugues by Bach Colleagues in London did polyphonic music recognition Audio -> events “an open research problem” Results vary from excellent to just recognizable One of worst-sounding cases is Prelude in G Major from the Well-Tempered Clavier, Book I

20. 19 OMRAS Audio-degraded Music IR Experiment Started with recording of 24 preludes and fugues by Bach Colleagues in London did polyphonic music recognition –“Convert to right [more than one note]: hard or very hard” –Results are recognizable, but… Listen (worst-sounding case)! Jeremy Pickens (UMass) converted results to MIDI file and used as queries against database of c. 3000 pieces in MIDI form –Method: “harmonic distributions” Outcome for “worst” case: the actual piece was ranked 1st! Average outcome: actual piece ranked c. 2nd

21. 20 OMRAS Research: Music Notation CMN often best form for musicians (even amateurs) –CMN sometimes essential for music researchers Searching CMN is obviously important... But almost no work on it so far! Why? –Specialized audience –Complexity => huge investment in programming –Lack of test collections Prospects for solving problems are good

22. 21 NightingaleSearch Nightingale ® is high-end commercial music editor for Macintosh –www.ngale.com NightingaleSearch inherits all normal functionality of Nightingale Searching commands use “Search Pattern” score as query Find next (“editor”) or find in database (“IR”) searching –Find in database is exact- or best-match Options: match pitch, match duration, etc. Does passage-level retrieval

23. 22 *Bach: “St. Anne” Fugue, with Search Pattern

24. 23 NightingaleSearch in Action With BachStAnne, exact-match OK, but... Best-match (threshhold 2) gives much better recall (of passages) with no loss of precision A harder example: user looking in a digital music library for “Twinkle, Twinkle, Little Star”(demo with a tiny personal library)

25. 24 *Mozart: Variations for piano, K. 265, on “Ah, vous dirais-je, Maman”

26. 25 *Suzuki: “Twinkle” Variations

27. 26 Typke’s MIR System Survey Rainer Typke’s “MIR Systems: A Survey of Music Information Retrieval Systems” lists many systems –http://mirsystems.info/ Commercial system: Shazam Some research systems can be used over the Web, incl.: –C-Brahms –Meldex/Greenstone –Mu-seek –MusicSurfer –Musipedia/Tuneserver/Melodyhound –QBH at NYU –Themefinder

28. 27 Machinery to Evaluate Music-IR Research Problem: how do we know if one system is really better than another, or an earlier version? Solution: standardized tasks, databases, evaluation –In use for speech recognition, text IR, question answering, etc. Important example: TREC (Text Retrieval Conference) For music IR, we now have... IMIRSEL (International Music Information Retrieval Systems Evaluation Laboratory) project –http://www.music-ir.org/evaluation/ MIREX (Music IR Evaluation eXchange) modeled on TREC –2005: audio only –2006: audio and symbolic

29. 28 Collections (a.k.a. Databases) (1 of 2) Collections are improving, but very slowly For research: poor to fair –“Candidate Music IR Test Collections” http://mypage.iu.edu/~donbyrd/MusicTestCollections.HTML –Representation “CMN” vs. CMN For practical use: pathetic (symbolic) to good (pop audio) –Most are commercial, especially audio –Very little free/public domain –…especially audio! (cf. RWC) IPR issues are a total mess

30. 29 Collections (a.k.a. Databases) (2 of 2) Why is so little available? –Symbolic form: no efficient way to enter –Solution: OMR? AMR? research challenges –Music is an art! –Cf. “Searching CMN” slides: chicken & egg problem –IPR issues are a total mess

31. rev. Jan. 200630 6. Summary (1 of 2) Basic representations of music: audio, events, notation –Fundamental difference: amount of explicit structure Have very different characteristics => each is by far best for some users and/or application Converting to reduce structure much easier than to add Music in all forms very hard to handle mostly because of: –Units of meaning problem –Polyphony Both problems are much less serious with text

32. rev. Jan. 200631 6. Summary (2 of 2) Projects include –Audio-based: via recognition of polyphonic music (OMRAS, query-by-humming, etc.) –CMN-based: monophonic query vs. polyphonic database (emphasis on UI) (OMRAS) –Style-genre identification from audio –Creative applications: music IR for improvisation, etc. Machinery to evaluate research is coming along (MIREX) Collections –for research: poor to fair –For practical use: pathetic (symbolic) to good (pop audio) –improving, but… –Serious problems with IPR as well as technology