Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Automatic Dastgah Recognition Using Markov Models
| Title | Automatic Dastgah Recognition Using Markov Models |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Conference Name | 14th International Symposium on Computer Music Multidisciplinary Research (CMMR) |
| Authors | Cimarone, L. , Bozkurt B. , & Serra X. |
| Pagination | 51-58 |
| Conference Start Date | 14/10/2019 |
| Conference Location | Marseille |
| Abstract | This work focuses on automatic Dastgah recognition of monophonic audio recordings of Iranian music using Markov Models. We present an automatic recognition system that models the sequence of intervals computed from quantized pitch data (estimated from audio) with Markov processes. Classification of an audio file is performed by finding the closest match between the Markov matrix of the file and the (template) matrices computed from the database for each Dastgah. Applying a leave-one-out evaluation strategy on a dataset comprised of 73 files, an accuracy of 0.986 has been observed for one of the four tested distance calculation methods. |
| preprint/postprint document | https://doi.org/10.5281/zenodo.3403310 |
Additional material:
Software available from:
https://github.com/luciamarock/Dastgah-Recognition-System
