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  • 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
  • 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
  • 2016: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) - 15:08, 17 February 2016
  • 2016: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
    20 KB (2,950 words) - 15:14, 17 February 2016
  • 2017:MIREX2017 Results
    [https://www.music-ir.org/mirex/results/2017/mirex_2017_poster.pdf MIREX 2017 Overall Results * [[2017:Audio Fingerprinting Results|Audio Fingerprinting Results]]
    4 KB (517 words) - 11:20, 26 April 2018
  • 2018:Main Page
    ...Information Retrieval Evaluation eXchange (MIREX 2018). The International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) at [https://i ...g as part of [http://ismir2018.ircam.fr The 19th International Society for Music Information Retrieval Conference], ISMIR 2018, which will be held in Paris,
    9 KB (1,261 words) - 12:13, 22 August 2018
  • 2018:Audio Classification (Train/Test) Tasks
    = Audio Classification (Test/Train) tasks = ...ssification tasks that follow this Train/Test process. For MIREX 2018, six classification sub-tasks are included:
    11 KB (1,643 words) - 10:31, 7 May 2018
  • 2018: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 (1,314 words) - 10:37, 7 May 2018
  • 2018: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 (1,271 words) - 10:38, 7 May 2018
  • 2018:MIREX2018 Results
    [https://www.music-ir.org/mirex/results/2018/MIREX2018Poster.pdf MIREX 2018 Results Poster (PD * Audio Melody Extraction Results
    4 KB (476 words) - 16:18, 14 October 2020
  • 2019:Main Page
    ...Information Retrieval Evaluation eXchange (MIREX 2019). The International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) at [https://i ...art of [http://ismir2019.ewi.tudelft.nl The 20th International Society for Music Information Retrieval Conference], ISMIR 2019, which will be held in Delft,
    8 KB (1,245 words) - 15:46, 27 January 2020
  • 2019:Audio Classification (Train/Test) Tasks
    = Audio Classification (Test/Train) tasks = ...ssification tasks that follow this Train/Test process. For MIREX 2018, six classification sub-tasks are included:
    11 KB (1,643 words) - 17:00, 7 March 2019
  • 2019: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 (1,314 words) - 17:04, 7 March 2019
  • 2019: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 (1,271 words) - 17:05, 7 March 2019

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