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Experiments of the Paper "MORTY: A Toolbox for Mode Recognition and Tonic Identification"

Sertan Şentürk


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.57999", 
  "author": [
    {
      "family": "Sertan \u015eent\u00fcrk"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2016, 
        7, 
        14
      ]
    ]
  }, 
  "abstract": "<p>This package contains the complete experimental data explained in:</p>\n\n<blockquote>\n<p>Karakurt, A.,\u00a0\u015eent\u00fcrk S., &amp;\u00a0Serra X.\u00a0(In Press).\u00a0\u00a0MORTY: A Toolbox for Mode Recognition and Tonic Identification.\u00a03rd International Digital Libraries for Musicology Workshop.\u00a0</p>\n</blockquote>\n\n<p>Please cite the paper above, if you are using the data in your work.</p>\n\n<p>The zip file includes the\u00a0folds, features, training and testing data, results and\u00a0evaluation file. It is part of the experiments hosted in github (https://github.com/sertansenturk/makam_recognition_experiments/tree/dlfm2016) in the \u00a0folder call \".<strong>/data</strong>\". We host the experimental data\u00a0in Zenodo (http://dx.doi.org/10.5281/zenodo.57999)\u00a0separately due to the file size limitations in github.</p>\n\n<p>The files generated from audio recordings are labeled with 16 character long MusicBrainz IDs (in short \"MBID\"s) Please check\u00a0http://musicbrainz.org/ for more information about the unique identifiers.\u00a0The structure of the data in the zip file is explained below.\u00a0In the paths\u00a0given below <em>task</em> is the computational task (\"tonic,\" \"mode\" or \"joint\"),\u00a0<em>training_type</em>\u00a0is either \"single\" (-distribution per mode) or \"multi\" (-distribution per mode),\u00a0\u00a0<em>distribution</em>\u00a0is either \"pcd\" (pitch class distribution) or \"pd\" (pitch distribution), <em>bin_size</em>\u00a0is the bin size of the distribution in cents, <em>kernel_width</em>\u00a0is the standard deviation of the Gaussian kernel used in smoothing the distribution, <em>distance</em>\u00a0is either the distance or the dissimilarity metric,\u00a0<em>num_neighbors</em>\u00a0is the number or neighbors checked in <em>k</em>-nearest neighbor classification and\u00a0<em>min_peak</em> is the minimum peak ratio. 0 <em>kernel_width</em>\u00a0implies no smoothing. <em>min_peak\u00a0</em>always takes the value 0.15.\u00a0For a thorough explanation please refer to the companion page (http://compmusic.upf.edu/node/319) and the paper itself.</p>\n\n<ul>\n\t<li><strong>folds.json:\u00a0</strong>Divides the test dataset (https://github.com/MTG/otmm_makam_recognition_dataset/releases) into training and testing sets according to stratified 10-fold scheme. The annotations are also distributed to sets accordingly. The file is generated by\u00a0\u00a0the Jupyter notebook\u00a0<em>setup_feature_training.ipynb (4th code block)</em>\u00a0in the github experiments repository\u00a0(https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb).</li>\n\t<li><strong>Features: \u00a0</strong>The path is <strong>data/features/[distribution--bin_size--kernel_width]/[MBID--(hist </strong><em>or\u00a0</em><strong>pdf)].json</strong>. \"pdf\" stands for\u00a0probability\u00a0density function, which is used to obtain the multi-distribution models in the training step and \"hist\" stands for the histogram, which is used to obtain the single-distribution models in the training step. The features are extracted using the Jupyter notebook <em>setup_feature_training.ipynb (5th code block)</em>\u00a0in the github experiments repository\u00a0(https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li>\n\t<li><strong>Training:\u00a0</strong>The path is <strong>data/training/[training_type--distribution--bin_size--kernel_width]/fold(0:9).json]</strong>. There are 10 folds in each folder, each of which stores the training model (file paths of the <em>distribution</em>s\u00a0in \"multi\" <em>training_type</em>\u00a0or the <em>distribution</em>s itself in \"single\" <em>training_type</em>) trained for the fold using the parameter set. The training files are generated by the\u00a0Jupyter notebook\u00a0<em>setup_feature_training.ipynb (6th code block)</em>\u00a0in the github experiments repository\u00a0(https://github.com/sertansenturk/makam_recognition_experiments/blob/master/setup_feature_training.ipynb)</li>\n\t<li><strong>Testing: </strong>The path is <strong>data/testing/[task]/[training_type--distribution--bin_size--kernel_width--distance--num_neighbors--min_peak]</strong>. Each path has the folders <strong>fold(0:9)</strong>, which have the evaluation and the results files obtained from each fold.\u00a0The path also has the\u00a0<strong>overall_eval.json</strong>\u00a0file, which stores the overall\u00a0evaluation of the experiment.\u00a0The optimal value of\u00a0<em>min_peak </em>is selected in the 4th code block, testing is carried in the 6th code clock and the evaluation is done in the 7th\u00a0code block\u00a0in\u00a0the\u00a0Jupyter notebook\u00a0<em>testing_evaluation.ipynb</em>\u00a0in the github experiments repository (https://github.com/sertansenturk/makam_recognition_experiments/blob/master/testing_evaluation.ipynb).\u00a0<br>\n\t<strong>data/testing/\u00a0</strong>folder also contains a summary of all the experiments in\u00a0the\u00a0files\u00a0<strong>data/testing/evaluation_overall.json\u00a0</strong>and\u00a0<strong>data/testing/evaluation_perfold.json</strong>.\u00a0These files are created in MATLAB while running the\u00a0statistical significance scripts.\u00a0<strong>data/testing/evaluation_perfold.mat </strong>is the same with the json file of the same filename, stored for fast reading.</li>\n</ul>\n\n<p>For additional information please contact the authors.</p>\n\n<p>This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.</p>", 
  "title": "Experiments of the Paper \"MORTY: A Toolbox for Mode Recognition and Tonic Identification\"", 
  "type": "dataset", 
  "id": "57999"
}
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