IDEAS FOR I590/N564 INDEPENDENT PROJECTS updated 2 Feb. 2006 (DAB = Don Byrd) NB: A few of these are suitable for short (mid-term) projects, but most would be more appropriate for longer (end of term) projects or for all semester, i.e., to be started for the mid-term project and continued for the end-of-term. Also, I'm open to any other project relevant to the course. Please feel free to discuss any of this with me. "*" in front of an item = brand-new or with major changes recently. "!" indicates projects I'd especially like to see someone work on. 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). [topic: symbolic retrieval] - Attempt to improve NightingaleSearch's music searching and evaluate the results in some way, preferably by MIREX or standard TREC (Cranfield model) methods. [topic: symbolic retrieval] - Build a database (e.g., from MIDI files, or from an existing collection like CCARH's) of at least 500 music "documents"; build a suitable set of queries; and/or investigate how the choice of search parameters affect the results, as evaluated by MIREX or TREC methods. [topic: symbolic retrieval] - Same as above, but using one of the MIREX tasks, preferably with M2K/D2K. - Use M2K/D2K to investigate any music-representation or searching question. Could be done with or without programming... - Extend OMRAS harmonic distributions, as used in the "OMRAS Audio-degraded Music IR Experiment" (cf. the ISMIR 2002 paper by Pickens et al.; reference in the list of publications on my website) !- Convert an interesting group of pieces, say the Preludes and/or Fugues from Bach's Well-Tempered Clavier in the CCARH MuseData collection, to NightingaleSearch format; use it to study something appropriate to the pieces you have, e.g., to confirm or refute accepted wisdom about that music. !- Same as above, but using the Humdrum Toolkit, and you may be able to use music already available in a format Humdrum can use. - Investigate converting between representations of the same type (e.g., one type of notation to another); optionally write a program to implement your ideas. [topic: music representation] - Investigate converting between representations of different types (e.g., notation to MIDI); optionally write a program to implement your ideas. [topic: music representation] !- Most music-IR research to date has been on tonal, functional-harmony Western music; investigate how it could be extended to other music(s). !- *It's obvious that automated Schenkerian analysis would be incredibly valuable for music IR--but is it even possible in general, or with restrictions (e.g., only Anglo-American folksongs or 12-bar blues)? [topic: music analysis, music perception, cognitive science] !- Adapt Steve Larson's theory of musical forces to recognize similarity between melodies or even complete polyphonic pieces of music. !- Devise a way to test Steve Larson's theory of musical forces with a database of melodies. - Investigate clustering musical documents on whatever basis; this could be very useful for visualization, recommender or improvisations systems, etc. Cf. several papers from ISMIR and elsewhere, and techniques like Kohonen maps and spring embedding. !- Investigate user-interface issues in music searching, either content-based or bibliographic. One option would be to actually design a user interface. - Follow up on the ISMIR 2000 Mozart Varations survey: do it more scientifically, or at least investigate how that could be done, preferably by designing a valid experiment. [topic: relevance judgments] !- DAB's Extremes of CMN list (on my website) is interesting, but distributions would be much more revealing. Compute distributions for some of the items in the list. This could be based on the CCARH database (http://www.ccarh.org/), e.g., 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. - Work on any of the topics listed in a recent ISMIR Call for Papers (http://www.ismir.net/). - Investigate methods for finding music that is unplayable. (Yes, published music that is _clearly_ unplayable exists. For example, a Scriabin piano sonata includes a note that's above the range of any piano, and I believe that in one of his symphonies Beethoven asks the violins to play below their lowest note.) - Extend any work by DAB related to this course, e.g., OMRAS (www.omras.org). - Investigate how content-based and metadata-based searching could be combined from any standpoint: user interface, ranking, etc. !- Investigate the "Mickey Mouse Club theme" problem: to what extent is a music- searching program likely to find matches in inner voices that are of little or no interest because they're completely inaudible? (The answer may well depend on whether the program knows about the voices and does not look for matches that cross voices: see the "disastrous loss of precision" idea below.) !- Investigate what it would take to identify 12-bar blues in a collection of, say, MIDI files or Humdrum/kern files; preferably try out your technique. !- Investigate the extent to which a performer's chosen medium influences their perception of music. For example, do tuba, bassoon, and double-bass players tend to hear lower lines as more salient than flutists or violinists do? How about basses vs. sopranos? What about salience of rhythm vs. pitch, e.g., for drummers vs. other musicians? - Byrd & Crawford (2002) speculate on the disastrous loss of precision they believe would result from taking "matches" that cross voices as seriously as those that stay within a voice, without considering the audibility of the matches. Investigate and produce evidence one way or the other. !- *Study a widely-used existing style-genre classification, e.g., that of All Music Guide, iTunes, Amazon.com, etc. Describe in some detail how it could be implemented by computer. Optionally, implement and test part of it, probably with a symbolic representation (audio is probably too difficult to do anything with by the end of the semester). [topic: music classification] - Study "national" style classification from either audio or CMN. What features that a program might really be able to identify make music sound French, Slavic, American, etc.? [topic: music classification] - Propose a new task for MIREX. Why is this task significant? How could entries be evaluated? [topic: evaluation] Almost certainly _not_ involving programming... - Walter Hewlett and DAB have found previously-unknown instances of the famous "B A C H" motive in the music of Bach and Douglas Hofstadter, respectively. DAB used NightingaleSearch. Use any other music-searching technology to find anything interesting in any database (the CCARH database is a good one for this purpose). - Investigate a basis for ranking music documents in search results. With music as with text, this is normally done by similarity, and justified via "relevance". But are these the best concepts for ranking music? - Extend or otherwise significantly improve DAB's table of candidate music-IR testbed databases (on my website). - Extend DAB's comparison of music to text, images, etc. by adding other media, more details, or both. - Test/compare existing audio music recognition programs; compare them to optical music recognition programs. Cf. www.music-notation.info/en/compmus/audio2midi.html . - Compare two or more music-notation encoding systems. This could be based on one done several years ago by Natalia Minibayeva, or could be completely new work. [topic: music representation] - Investigate and compare existing metadata formats for music, and/or design a new one. - Extend, improve, or just evaluate DAB and Eric Isaacson's music-representation requirements specification (created for Variations2). !- Annotate or otherwise significantly improve DAB's music-IR bibliography (on my website). When I taught this course in 2003, someone annotated 50 entries in terms of how useful they would likely be to someone in the class; more of that would be worthwhile, or more of the type of annotations some entries have now. - Compare classifications of music representations, e.g., DAB's, Selfridge-Field's, Castan's, Wiggins'; perhaps propose a new classification. [topic: music representation] - 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.). How could MIREX be improved? [topic: evaluation] !- List and discuss several of what you consider the most important unsolved problems of music IR. (I'll be glad to tell you what I think some of these problems are, but you're welcome to choose your own.) !- A former director of engineering for All Music Guide recently (Sept. 2005) said, in so many words, that programs that do automatic genre classification from audio are probably finding _something_, and something useful, but it may not be genres as people understand them. Investigate and report on the accuracy of this statement. [topic: music classification] !- There is very little agreement among existing style-genre classifications: the numbers of categories varies wildly (All Music Guide has 34, iTunes 37, Amazon.com 23, etc.)--and even those numbers overestimate the agreement, since they're not all "flat" lists, and some confuse styles and forms. Compare at least three existing classifications, and comment on which seems most practical for computer implementation and why. [topic: music classification] !- Study existing sets of relevance judgments for music and/or create a new set. [topic: relevance judgments] !- *Find a small number, but at least five or six, pieces of music each of which is, in your opinion, as different as possible from all the others. Once you've chosen the pieces, either justify or refute your claim that each is as different as possible from the others. If at all possible, the justifying or refuting should have some objective basis, i.e., a survey of listeners. [topic: music classification]