@InProceedings{karakurt2016morty_dlfm,
  Title                    = {{MORTY}: A toolbox for mode recognition and tonic identification},
  Author                   = {Karakurt, Altu{\u g} and {\c S}ent{\"u}rk, Sertan and Serra, Xavier},
  Booktitle                = {Proceedings of the 3rd International Digital Libraries for Musicology Workshop (DLfM 2016)},
  Year                     = {2016},

  Address                  = {New York, NY, USA},
  Pages                    = {9--16},
  Publisher                = {ACM},

  Abstract                 = {In the general sense, mode defines the melodic framework and tonic acts as the reference tuning pitch for the melody in the performances of many music cultures. The mode and tonic information of the audio recordings is essential for many music information retrieval tasks such as automatic transcription, tuning analysis and music similarity and automatic description of digital music libraries applied to these cultures. In this paper we present MORTY, an open source toolbox for mode recognition and tonic identification. The toolbox implements a generalized variant of two state-of-the-art methods based on pitch distribution analysis. The algorithms are designed in a generic manner such that they can be easily optimized according to the culture-specific aspects of the studied music tradition. We test the generalized methodology systematically on the largest mode recognition dataset curated for Ottoman-Turkish makam music so far, which is composed of 1000 recordings in 50 modes. We obtained 95.8\%, 71.8\% and 63.6\% accuracy in tonic identification, mode recognition and joint estimation of the mode and tonic tasks, respectively. We additionally present recent experiments on Carnatic and Hindustani music in comparison with several methodologies recently proposed for raga/raag recognition. We believe that our toolbox would be used as a benchmark for future methodologies proposed for mode recognition and tonic identification, especially for music traditions in which these computational tasks have not been addressed yet.},
  Acmid                    = {2970054},
  Doi                      = {10.1145/2970044.2970054},
  File                     = {:publications/karakurt2016morty_dlfm.pdf:PDF},
  ISBN                     = {978-1-4503-4751-8},
  Keywords                 = {Carnatic Music, Hindustani Music, Mode recognition, Open Source Software, Ottoman-Turkish makam music, Pitch Class Distribution, Reproducibility, Tonic Identification, Toolbox, k-nearest neighbors classification},
  Url                      = {http://doi.acm.org/10.1145/2970044.2970054}
}