IDEAS FOR I548/N560 SEMESTER PROJECTS - 15 Sept. 2007 (DAB = Don Byrd)

Here are some ideas for semester projects for I548. I'd be happy to consider
anything else you think is relevant to the course; in fact, it's better in some ways
if students make their own projects up. However, I'll expect you to convince me your
project can demonstrate your mastery of several of our "core competencies". Please
feel free to discuss any of this with me at any time.

For background information, see my Information Sources for Music Informatics
Students page
(http://www.informatics.indiana.edu/donbyrd/Teach/GeneralInformationSources.HTML)
and my Music IR and Music Informatics Bibliography
(http://www.informatics.indiana.edu/donbyrd/DonMusicIRBibliography.HTML). In
addition, some of the entries include references of their own.


Requiring Programming
---------------------

- Write a simple program to compute the similarity between two melodies, rhythm
patterns, chord progressions, or even complete pieces, represented in some symbolic
form (such a program could be used as the basis for a music-retrieval program). With
what types of music would you expect your program to work well? With what types
would you expect it to work at least somewhat? Test it against a well-thought-out
collection of test files, and report on the results.  [topic: symbolic retrieval]

- Make significant improvements to an existing program that analyzes audio in some
way. Most such programs are probably too difficult for you to do anything with,
given the constraints of time and your limited knowledge, but there are lots of simple
ones around. DAB's R program DescribeAudioSegments (one of the "R Program Examples"
on my website) is an unusually simple -- a better word might be "crude" -- example
of such a program. In what way(s) is your version an improvement? Test yours against
the original with a well-thought-out collection of test files, and report on the
results.

- Make significant improvements to an existing plugin for the Sonic Visualiser
(www.sonicvisualiser.org). Do a user study to determine whether your changes
really are improvements with either subjective or objective metrics.


Could Be Done With Or Without Programming
-----------------------------------------

- Adapt Steve Larson's "theory of musical forces" (see, e.g., "Musical Forces and
Melodic Expectations: Comparing Computer Models and Experimental Results"; abstract at
http://caliber.ucpress.net/doi/abs/10.1525/mp.2004.21.4.457) to recognize similarity
between melodies or even complete polyphonic pieces of music. Devise a way to test
your version of the theory; test it with a well-thought-out collection of music --
it might be possible to do this manually with a small collection -- and report on
the results. [topic: music analysis]

- Devise a way to test Steve Larson's theory of musical forces with a database of
melodies. Run it with a well-thought-out collection of music -- it might be possible
to do this manually with a small collection -- and report on the results. [topic:
music analysis]

- It's obvious that automated Schenkerian analysis, even going just two or three
levels down from the surface, would be incredibly valuable for music IR; however, a
general, style-independent solution is probably not possible in the forseeable
future. But how about a solution for a very limited range of styles -- e.g., only
Anglo-American folksongs or 12-bar blues? Cf. "controllers" in David Cope's EMI
system. Study the problem and report on what you find in as scholarly a way as
possible; preferably write a program and test it, but at least say just what your
method _would_ do for some real music. [topic: music analysis, music perception,
cognitive science]

- Investigate clustering musical documents on whatever basis; this could be very
useful for visualization, recommender or improvisation systems, etc. Cf. several
papers from ISMIR and elsewhere, and techniques like MDS, PCA, Kohonen maps, and
spring embedding.

- Investigate converting between representations of different types (e.g., notation
to MIDI); if possible, write a program to implement your ideas, and test it on a
well-thought-out collection of music. [topic: music representation]

- Investigate user-interface issues in music searching, either content-based or
bibliographic, by designing a user interface and testing it on a number of
volunteers. The testing can be for either subjective or objective factors, but
it should conform to the scholarly method.

- DAB's Extremes of CMN list (on my website) is interesting, but _distributions_ for
some collection of music and one or more of the features (e.g., written pitch
or duration, or just number of augmentation dots!) showing how often various values
occur in a significant body of music would be much more revealing; such distributions
could be useful in statistical authorship studies, for example. Compute distributions
for some of the items in the list and investigate how the resulting information
could be useful. For a music collection, you could use the CCARH database
(http://www.ccarh.org/), with kern data (http://kern.humdrum.net/) accessed via the
Humdrum toolkit, or with MusicXML data (available from me) accessed via a program of
your own. In any case, the programming part of this is relatively easy.

- Propose a new task for MIREX. Why is this task significant? How could entries
be evaluated? One possible task would be OMR. [topic: evaluation]

- Investigate a basis for ranking music documents in search results. With music, as
with text, this is normally done by similarity between the results and the query,
and justified via "relevance". But are these the best concepts for ranking music?
Present solid evidence one way or the other, perhaps via a user study.


Probably Not Involving Programming
----------------------------------

- The "music as different as possible" problem. The idea is to find six or so
pieces of music each of which is considered by listeners to be as different as
possible from all the others; the hard part is to find objective evidence for the
choices! Once you've chosen them, either justify or refute your claim that each is
as different as possible from the others on a basis that's as objective as possible,
almost certainly via a survey of listeners. Better, use such a measure to find the
pieces in the first place. In either case, "as objective as possible" is not likely
to be very objective: discuss the inherent limitations of objectivity here. This
would be a step towards defining a musical style space. [topic: music classification]

- Study perception of musical-instrument timbre. For example, there's evidence that
people can identify instruments from note _attacks_ more reliably than from note
"steady states" (sustains); can you find strong evidence one way or the other? Of
course it depends on the definition of "attack". Another question: if you "morph"
notes from one instrument into notes from another, at what point (in terms of
the physical characteristics of the sound) do people switch from hearing one to the
other? And how well do people agree? You might expect players of an instrument to
be more likely to think intermediate sounds are their instrument -- or more likely
to think they're _not_ their instrument. Do a user study.

- Investigate abstract spaces relevant to music, e.g., timbre or musical style.
To my knowledge, published work on timbre space has been around since the
1970's, but little has been done in many years. Far less has been done on musical
style spaces, which are a far more difficult problem -- but making progress from
almost nothing shouldn't be that hard!

- Study how MIREX works. Compare it to similar undertakings in other domains (TREC
for text IR, the standard speech-recognition and question-answering tests, etc.).
Say in detail how MIREX be improved. Can you give a convincing argument that your
changes would improve MIREX? This seems like a difficult question of experimental
methodology. One way might be to show what would have happened in a previous MIREX
with your changes, but I'm skeptical that could be done in a convincing way. [topic:
evaluation]