2 datasets found
  1. P

    MusicNet Dataset

    • paperswithcode.com
    • opendatalab.com
    • +1more
    Updated Nov 3, 2021
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    MusicNet Dataset [Dataset]. https://paperswithcode.com/dataset/musicnet
    Explore at:
    Dataset updated
    Nov 3, 2021
    Authors
    John Thickstun; Zaid Harchaoui; Sham Kakade
    Description

    MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

  2. a

    musicnet.tar.gz

    • academictorrents.com
    bittorrent
    Updated Mar 7, 2020
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    John Thickstun and Zaid Harchaoui and Dean P. Foster and Sham M. Kakade (2020). musicnet.tar.gz [Dataset]. https://academictorrents.com/details/d2b2ae5e3ec4fd475d6e4c517d4c8752a7aa8455
    Explore at:
    bittorrentAvailable download formats
    Dataset updated
    Mar 7, 2020
    Dataset authored and provided by
    John Thickstun and Zaid Harchaoui and Dean P. Foster and Sham M. Kakade
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note s position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
MusicNet Dataset [Dataset]. https://paperswithcode.com/dataset/musicnet

MusicNet Dataset

Explore at:
Dataset updated
Nov 3, 2021
Authors
John Thickstun; Zaid Harchaoui; Sham Kakade
Description

MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note in every recording, the instrument that plays each note, and the note's position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.

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