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  • 2013:Audio K-POP Mood Classification
    = K-POP Mood Classification = ...y effective on predicting mood labels by American annotator and predicting mood labels by Korean annotator.
    9 KB (1,315 words) - 12:17, 19 January 2014
  • 2013:MIREX2013 Results
    [https://www.music-ir.org/mirex/results/2013/mirex_2013_poster.pdf MIREX 2013 Overall Results * [https://www.music-ir.org/nema_out/mirex2013/results/act/composer_report/ Audio Classical Composer Identification Results ]  
    5 KB (688 words) - 23:23, 24 October 2014
  • 2014:Main Page
    ...tional Music Information Retrieval Systems Evaluation Laboratory ([https://music-ir.org/evaluation IMIRSEL]) at the Graduate School of Library and Informati ...part of [http://ismir2014.ismir.net/ The 15th International Conference on Music Information Retrieval], ISMIR 2014, which will be held in Taipei, Taiwan, t
    9 KB (1,330 words) - 14:32, 24 September 2014
  • 2014:Audio Classification (Train/Test) Tasks
    = Audio Classification (Test/Train) tasks = ...sification tasks that follow this Train/Test process. For MIREX 2014, five classification sub-tasks are included:
    11 KB (1,608 words) - 14:21, 7 January 2014
  • 2014:Audio K-POP Genre Classification
    = K-POP Genre Classification = ...r other cultural) music can be applied to K-POP music as well; 2)to see if classification models can be equally effective on predicting genre labels by American anno
    8 KB (1,272 words) - 17:16, 17 September 2014
  • 2014:Audio K-POP Mood Classification
    = K-POP Mood Classification = ...y effective on predicting mood labels by American annotator and predicting mood labels by Korean annotator.
    9 KB (1,315 words) - 14:27, 7 January 2014
  • 2014:Audio Tag Classification
    ...ver, multiple tags may be applied to each example rather than single-label classification. Audio tag classification was first run at MIREX 2008 [[2008:Audio_Tag_Classification]] and as a spec
    21 KB (2,970 words) - 14:32, 7 January 2014
  • 2014:Task Captains
    |[[2014:Audio Beat Tracking]] |[[2014:Audio Chord Estimation]]
    2 KB (300 words) - 21:27, 20 October 2014
  • 2014:MIREX2014 Results
    [https://www.music-ir.org/mirex/results/2014/mirex_2014_poster.pdf MIREX 2014 Overall Results * [https://www.music-ir.org/nema_out/mirex2014/results/act/composer_report/ Audio Classical Composer Identification Results ]  
    6 KB (836 words) - 12:56, 31 October 2014
  • 2014:Timetable
    |[[2014:Audio Beat Tracking]] |[[2014:Audio Chord Estimation]]
    1 KB (137 words) - 13:54, 12 March 2014
  • 2015:Main Page
    ...tional Music Information Retrieval Systems Evaluation Laboratory ([https://music-ir.org/evaluation IMIRSEL]) at the Graduate School of Library and Informati ...g as part of [http://ismir2015.uma.es The 16th International Conference on Music Information Retrieval], ISMIR 2015, which will be held in Malaga, Spain, Oc
    9 KB (1,308 words) - 23:02, 17 February 2016
  • 2015:Audio Classification (Train/Test) Tasks
    = Audio Classification (Test/Train) tasks = ...sification tasks that follow this Train/Test process. For MIREX 2015, five classification sub-tasks are included:
    11 KB (1,608 words) - 11:39, 7 August 2015
  • 2015:Audio K-POP Mood Classification
    = K-POP Mood Classification = ...y effective on predicting mood labels by American annotator and predicting mood labels by Korean annotator.
    9 KB (1,315 words) - 21:17, 26 March 2015
  • 2015:Audio K-POP Genre Classification
    = K-POP Genre Classification = ...r other cultural) music can be applied to K-POP music as well; 2)to see if classification models can be equally effective on predicting genre labels by American anno
    8 KB (1,272 words) - 21:18, 26 March 2015
  • 2015:Audio Tag Classification
    ...ver, multiple tags may be applied to each example rather than single-label classification. Audio tag classification was first run at MIREX 2008 [[2008:Audio_Tag_Classification]] and as a spec
    21 KB (2,970 words) - 21:23, 26 March 2015
  • User:Thomas Lidy
    Researcher in Music IR at Vienna University of Technology (TU WIEN) since 2004 ...ine learning (most recently deep learning) for automatic classification of music.
    341 bytes (52 words) - 18:50, 12 August 2015
  • 2015:MIREX2015 Results
    [https://www.music-ir.org/mirex/results/2015/mirex_2015_poster.pdf MIREX 2015 Overall Results * [https://www.music-ir.org/nema_out/mirex2015/results/act/composer_report/ Audio Classical Composer Identification Results ]  
    6 KB (750 words) - 10:20, 26 October 2015
  • 2016:Main Page
    ...tional Music Information Retrieval Systems Evaluation Laboratory ([https://music-ir.org IMIRSEL]) at the Graduate School of Library and Information Science ...art of [https://wp.nyu.edu/ismir2016/ The 17th International Conference on Music Information Retrieval], ISMIR 2016, which will be held in New York City, US
    9 KB (1,271 words) - 12:20, 7 May 2018
  • 2016:Audio Classification (Train/Test) Tasks
    = Audio Classification (Test/Train) tasks = ...ssification tasks that follow this Train/Test process. For MIREX 2016, six classification sub-tasks are included:
    11 KB (1,643 words) - 15:04, 17 February 2016
  • 2016:Audio K-POP Mood Classification
    = K-POP Mood Classification = ...y effective on predicting mood labels by American annotator and predicting mood labels by Korean annotator.
    9 KB (1,315 words) - 15:06, 17 February 2016

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