Transcript Organization of Musical Information

Organization of and Searching in
Musical Information
(a.k.a. Music Representation, Searching, and Retrieval)
Donald Byrd
School of Informatics & School of Music
Indiana University
16 January 2007
1
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
rev. Jan. 2006
2
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
3
2. Basic Representations of Music & Audio
Digital Audio
Audio (e.g., CD, MP3):
like speech
Time-stamped
Time-stamped
Events Events
(e.g., MIDI file): like
unformatted text
Music
Notation
Music Notation:
like
text with complex
formatting
4
Basic Representations of Music & Audio
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
information)
much (complete
voicing information)
Avg. rel. storage
2000
1
10
Convert to left
-
OK job: easy
Good job: hard
OK job: easy
Good job: hard
Convert to right
1 note: pretty easy
OK job: hard
other: hard or very hard
-
Ideal for
music
bird/animal sounds
sound effects
speech
music
music
rev. Jan. 2006
5
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!
6
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.
7
How People Find Text Information
Query
Database
understanding
understanding
Database
concept s
Query
concept s
matching
Result s
•What user wants is almost always concepts…
•But computer can only recognize words
8
How Computers Find Text Information
Query
Database
Stemming, stopping,
query expansion, etc.
(no understanding )
(no understanding )
matching
Result s
•“Stemming, stopping, query expansion” are all tricks to increase
precision & recall (avoid false negatives & false positives) due to
synonyms, variant forms of words, etc.
9
3. Why is Musical Information
Hard to Handle?
1. Units of meaning: not clear there are any—assuming music
even has meaning! (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
Units of Meaning (Problem 1)
• Handling text information nearly always via words
– “What we want is concepts; what we have is words”
• 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
rev. Jan. 2007
11
Units of Meaning (Problem 1)
•
•
•
•
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 rhythm
– Ignores relationships between voices (harmony)
– Probably little correlation with semantics
• Are chords like words? (Christy Keele)
– If so, chord progressions may be like sentences
– In some ways, but ignores melody & rhythm, most relevant for
tonal music, etc.
• Anyway, in much music, pitch isn’t important, and/or notes
aren’t important!
rev. Jan. 2007
12
Independent Voices in Music
(Problem 2)
J.S. Bach: “St. Anne” Fugue, beginning
13
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
Performance (time goes from left to right):
M: What I fancy is a rare steak. Gret?
I:
G:
I haven’t been...
I am of course a member of the Church of England.
Potatoes.
14
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
15
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
16
4. Music vs. Text and Other Media
———— Explicit Structure ————
least
medium
most
Salience
increasers
Music
audio
events
notation
loud; thin texture
Text
audio (speech)
ordinary
text with markup
written text
“headlining”: large,
bold, etc.
Images
photo, bitmap
PostScript drawing-program
file
bright color
MPEG?
motion, etc.
Video
videotape
w/o sound
Biological DNA sequences,
data
3D protein structures
Premiere file
MEDLINE abstracts ??
17
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
18
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
19
Course Overview
• II. Organization of Musical Information (music
representation)
– “What we want is concepts; what we have is words”
– Audio, MIDI, notation
• III. Finding Musical Information
– A Similarity Scale for Content-Based Music IR
• IV. Musical Similarity and Finding Music by Content
• V. Finding music via Metadata
– Digital music libraries (Variations2), iTunes, etc.
– Music recommender systems
Jan. 2007
20
1. Programming in R: No Problem!
•
•
•
•
R is very interactive: can use as powerful calculator
Assignments will be fairly simple
Much help available: from Don & other students
Why R?
–
–
–
–
–
–
–
NOT because it's great for statistics!
easy to do simple things with it, including graphs and handling audio files
probably not good for complex programs
free, & available for all popular operating systems
very interactive => easy to experiment
has good documentation
In use in other Music Informatics classes, & standardizing is good
21
1. Rudiments of R
• Originally for statistics; good for far more
• How to get R
– Web site: http://cran.us.r-project.org/
– Versions for Linux, Mac OS X, Windows
– Already on STC Windows machines; will be in M373
• Tutorial:
• http://xavier.informatics.indiana.edu/~craphael/teach/symb
olic_music/
• Can use R interactively as a powerful graphing, musicing,
etc. calculator
• …but it’s not perfect: sometimes very cryptic
3 Sep. 2006
22
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
23
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
24
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
25
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
26
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
rev. Jan. 2006
27
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
rev. Jan. 2006
28