Difference between revisions of "Learning Indian Classical Using Sequential Models"
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| − | 1. http://www.slideshare.net/butest/music-and-machine-learning | + | 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] | + | 2. [http://queens.db.toronto.edu/~cmishra/tansen.pdf TANSEN : A SYSTEM FOR AUTOMATIC RAGA IDENTIFICATION] |
Revision as of 00:44, 23 September 2011
Team Members
Project Idea
Indian Classical music is a very structured when it comes to melody. A composition is (generally) within a constraints of a 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.
Previous Work
Hidden Markov Models have been used for raga classification. The first step is to transcribe the music. This is either done by playing a midi keyboard, or if a performance is used, using frequency identification techniques. Such techniques are specified in [2].
Dataset
There are midi files available at http://www.cse.iitk.ac.in/users/tvp/music/.
References
1. http://www.slideshare.net/butest/music-and-machine-learning 2. TANSEN : A SYSTEM FOR AUTOMATIC RAGA IDENTIFICATION
