View source for Cohen Courses:Learning Indian Classical Using Sequential Models

= Team Members = 

* [[User:dkulkarn|Dhananjay Kulkarni]]
* [[User:tkumar|Tarun Kumar]]

= Project Idea = 

Indian Classical music is a very structured  when it comes to melody. A composition is (generally) within a constraints of a [http://en.wikipedia.org/wiki/Raga raag]. It has a specific grammar, which lends the emotions to the composition. This aspect of music lends an interesting application of sequential models for note prediction, and raga classification.

== Problem Statement == 

=== Pakad === 

Pakad is a string of notes characteristic to a Raga to which a musician frequently returns while improvising in a performance. A pakad has the potential to illustrate the grammar and aesthetics of a raga. For example consider raga [http://en.wikipedia.org/wiki/Bageshree Bageshree]. The pakad is F G A F D# D C. It can be rendered in various ways as - 

* F G A F D# F DC
* F G A F G D# F D C 
* F G A , D# F D C

The following are valid sequences in Bageshree, but they are not pakads - 

* F G A G F D# D C
* F A G F D# F D C

Since pakad enforces a raga, the objective would be to identify a pakad in a sequence of notes.

''Questions from William'':
* Don't you need duration and stress as well as the notes?
> Yes. These are additional features. Since I'm using midi files, I do have the stress (velocity) and the duration of the notes (which will be preserved in the annotation). But the baseline doesn't need it.
* What does the comma mean?
> It means a stop. 
* How do you plan to encode this? as a BIO labeling for notes?
> Right now, I'm planning with attr/non-attr type labels for each notes. I haven't figured out what difference would BIO make.
* How hard is it to go from midi to a sequence of nodes (maybe with stress and duration, if you need that)?
> I have the code in place for that. It identifies the note; the duration and the stress.

Sorry for all the questions - it's partly my unfamiliarity with the domain... --[[User:Wcohen|Wcohen]] 14:47, 11 October 2011 (UTC)

= Baseline = 

In [2], the pakad matching was done using <math>\delta</math>-Occurence with <math>\alpha</math>-Bounded Gaps. This however, fails for the two sequences displayed above.

= Grand Idea =

The grand idea is to view this task as a sequence alignment problem. There has been considerable work in machine translation. The challenge would be to adapt this work.

Question from William: ie, you would be learning a similarity metric? or constructing alignments between a midi file and some designated prototypes? please explain in more detail what the inputs and outputs of the system would be. --[[User:Wcohen|Wcohen]] 14:43, 11 October 2011 (UTC)
> I'm constucting alignments between a midi file and designated prototypes. 

= Dataset =

There are midi files available at http://www.cse.iitk.ac.in/users/tvp/music/. These will be manually annotated for pakads.

Questions from William: 
* how long will it take to do the annotation (do you have a clear idea yet)?  It seems like this might be a hard annotation task, since you're labeling subsequences of the song rather than just adding labels to a complete song.
> I plan to complete the annotations by this weekend. I'm not doing inter-annotator agreement to start with. 
* What will be your baseline method? I see the related work, but I don't know if that is a difficult thing to re-implement or not.  Is there some sort of off-the-shelf learning method that can be used?
> I want to compare it with the existing technique to demonstrate that using sequence alignment makes sense.

--[[User:Wcohen|Wcohen]] 14:42, 11 October 2011 (UTC)

= References = 

1. http://www.slideshare.net/butest/music-and-machine-learning

2. [http://queens.db.toronto.edu/~cmishra/tansen.pdf TANSEN : A SYSTEM FOR AUTOMATIC RAGA IDENTIFICATION]

3. [http://www.stringology.org/event/2002/p7.html C. S. Iliopoulos and M. Kurokawa: "StringMatching with Gaps for Musical Melodic Recognition": Proc. Prague Stringology Conference, pp. 55-64: 2002.]