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100+ datasets found
  1. Important features of music streaming services in the U.S. 2020

    • www.statista.com
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  2. H

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  4. Primary ITT Music Materials

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  5. r

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  6. d

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  7. Data from: MUSDB18 - a corpus for music separation

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  8. r

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  12. O

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  13. Spotify Song Attributes

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  14. Data from: Cognitive Effects of Music and Dance Training in Children

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  15. MUSIC FOR ALL INC, fiscal year ending Feb. 2018

    • projects.propublica.org
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  17. F

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  18. d

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  19. d

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  20. f

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  21. MANHATTAN SCHOOL OF MUSIC, fiscal year ending June 2017

    • projects.propublica.org
  22. F

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    • fred.stlouisfed.org
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  23. d

    State Library of Queensland - Music Queensland - World War 1

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  24. H

    Survey Data on Memorable Experiences with Sad Music – Reasons, Reactions and...

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  25. a

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  26. O

    State Library of Queensland - Music Queensland - World War 1

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  27. Subscribers to Leeds international chamber music

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  28. H

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    • dataverse.harvard.edu
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  29. H

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    pdf, tsv
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  30. p

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  31. t

    United States Exports to Romania of Music, Printed or in Manuscript (Whether...

    • tradingeconomics.com
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  32. Individual Music education hubs

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  33. b

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  34. o

    A dataset recorded during development of an affective brain-computer music...

    • openneuro.org
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  35. Associated Press/Rolling Stone Magazine Poll: Music [Roper #31090061]

    • ropercenter.cornell.edu
    ascii file +1
    Updated Nov 30, 2009
  36. The SHAMSA database 1.0 – Sources for the History and Analysis of...

    • zenodo.org
    xlsx
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  37. d

    Data from: Creating a multi-track classical music performance dataset for...

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  38. a

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  39. o

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    Updated Oct 15, 2019
  40. a

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  41. Music features

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  42. F

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  43. 18,393 Pitchfork Reviews

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  44. t

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  45. YouTube: most watched Latin artists' music videos 2020

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  46. 4

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  47. CBS News Poll: Super Bowl/Music/Olympics [Roper #31113400]

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  48. Leading Android music apps in Germany 2020, by downloads

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  49. Rapid Brain Responses to Familiar vs. Unfamiliar Music – an EEG and...

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  50. d

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  51. a

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  52. N

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  53. Roper Commercial # 8: Radio Music [Roper #31097026]

    • ropercenter.cornell.edu
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  54. a

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  55. r

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  56. CURTIS INSTITUTE OF MUSIC, fiscal year ending May 2017

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  57. d

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  58. H

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  59. d

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  60. SSRS Spotlight Poll: Avengers/Music/Tooth Fairy [Roper #31116544]

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  61. D

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  62. D

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  63. DAVIDSON FOUNDATION FOR MUSIC DANCE AND DRAMATIC ARTS, fiscal year ending...

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  64. f

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  67. MUSIC ASSOCIATES OF ASPEN INC, fiscal year ending Sept. 2017

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  68. d

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  69. e

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  70. e

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  71. r

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  72. Policy notes: local popular music in global creative economies: data

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  73. r

    I. Opposition of Folk and Palace/Temple Music in Antiquity

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  74. a

    Smithsonian Folkways Holiday Music

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  75. National Library of Australia Sheet Music collection

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  76. N

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  77. o

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  78. d

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    • datadiscoverystudio.org
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  79. g

    Data from: Generating music with resting-state fMRI data

    • gigadb.org
    Updated Oct 26, 2016
  80. N

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    • neurovault.org
    zip
    Updated Mar 21, 2020
  81. music for the film Cetaphobia

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  82. N

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  83. China Retail Price Index: Urban: Household Appliance, Music and Video...

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  84. H

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  85. F

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  86. t

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  87. N

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  88. t

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  89. o

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  90. m

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  91. r

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  92. a

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    Updated Dec 12, 2012
  93. MSMD - Multimodal Sheet Music Dataset

    • zenodo.org
    zip
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  94. r

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  95. t

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  96. f

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  97. N

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  98. r

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  99. D

    Deciphering Lyrical Topics in Music and Their Connection to Audio Feature...

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  100. r

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Data from: MUSDB18 - a corpus for music separation

Related Article
16 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated Dec 17, 2017
Dataset provided by
Fraunhofer Institute for Digital Media Technologyhttp://www.idmt.fraunhofer.de/
New York Universityhttps://www.nyu.edu/
Gracenote, USA
INRIA and LIRMM, University of Montpellier
Authors
Rafii, Zafar; Liutkus, Antoine; Fabian-Robert Stöter; Mimilakis, Stylianos Ioannis; Bittner, Rachel
Description

The sigsep musdb18 data set consists of a total of 150 full-track songs of different styles and includes both the stereo mixtures and the original sources, divided between a training subset and a test subset.

Its purpose is to serve as a reference database for the design and the evaluation of source separation algorithms. The objective of such signal processing methods is to estimate one or more sources from a set of mixtures, e.g. for karaoke applications. It has been used as the official dataset in the professionally-produced music recordings task for SiSEC 2018, which is the international campaign for the evaluation of source separation algorithms.

musdb18 contains two folders, a folder with a training set: “train”, composed of 100 songs, and a folder with a test set: “test”, composed of 50 songs. Supervised approaches should be trained on the training set and tested on both sets.

All files from the musdb18 dataset are encoded in the Native Instruments stems format (.mp4). It is a multitrack format composed of 5 stereo streams, each one encoded in AAC @256kbps. These signals correspond to:

  • 0 - The mixture,
  • 1 - The drums,
  • 2 - The bass,
  • 3 - The rest of the accompaniment,
  • 4 - The vocals.

For each file, the mixture correspond to the sum of all the signals. All signals are stereophonic and encoded at 44.1kHz.

As the MUSDB18 is encoded as STEMS, it relies on ffmpeg to read the multi-stream files. We provide a python wrapper called stempeg that allows to easily parse the dataset and decode the stem tracks on-the-fly.

If you use the MUSDB dataset for your research - Cite the MUSDB18 Dataset

@misc{MUSDB18,
  author       = {Rafii, Zafar and
                  Liutkus, Antoine and
                  Fabian-Robert St{\"o}ter and
                  Mimilakis, Stylianos Ioannis and
                  Bittner, Rachel},
  title        = {The {MUSDB18} corpus for music separation},
  month        = dec,
  year         = 2017,
  doi          = {10.5281/zenodo.1117372},
  url          = {https://doi.org/10.5281/zenodo.1117372}
}

If compare your results with SiSEC 2018 Participants - Cite the SiSEC 2018 LVA/ICA Paper

@inproceedings{SiSEC18,
  author="St{\"o}ter, Fabian-Robert and Liutkus, Antoine and Ito, Nobutaka",
  title="The 2018 Signal Separation Evaluation Campaign",
  booktitle="Latent Variable Analysis and Signal Separation:
  14th International Conference, LVA/ICA 2018, Surrey, UK",
  year="2018",
  pages="293--305"
}
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