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MNIST Classification

All metadata and features for all tracks are distributed in (342 MiB).The below tables can be used with......

数据结构 ? 1001.5G

    All metadata and features for all tracks are distributed in (342 MiB). The below tables can be used with pandas or any other data analysis tool. See the paper or the usage.ipynb notebook for a description.

    • tracks.csv: per track metadata such as ID, title, artist, genres, tags and play counts, for all 106,574 tracks.

    • genres.csv: all 163 genres with name and parent (used to infer the genre hierarchy and top-level genres).

    • features.csv: common features extracted with librosa.

    • echonest.csv: audio features provided by Echonest (now Spotify) for a subset of 13,129 tracks.

    Then, you got various sizes of MP3-encoded audio data:

    1. 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB)

    2. 25,000 tracks of 30s, 16 unbalanced genres (22 GiB)

    3. 106,574 tracks of 30s, 161 unbalanced genres (93 GiB)

    4. 106,574 untrimmed tracks, 161 unbalanced genres (879 GiB)

    See the wiki (or #41) for known issues (errata).


    The following notebooks, scripts, and modules have been developed for the dataset.

    1. usage.ipynb: shows how to load the datasets and develop, train, and test your own models with it.

    2. analysis.ipynb: exploration of the metadata, data, and features. Creates the figures used in the paper.

    3. baselines.ipynb: baseline models for genre recognition, both from audio and features.

    4. features extraction from the audio (used to create features.csv).

    5. webapi.ipynb: query the web API of the FMA. Can be used to update the dataset.

    6. creation.ipynb: creation of the dataset (used to create tracks.csv and genres.csv).

    7. creation of the dataset (long-running data collection and processing).

    8. helper functions and classes.

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