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FMA:音乐分析数据集

FMA:音乐分析数据集

1001.5G
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MNIST Classification

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

数据结构 ? 1001.5G

    README.md

    All metadata and features for all tracks are distributed in fma_metadata.zip (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. fma_small.zip: 8,000 tracks of 30s, 8 balanced genres (GTZAN-like) (7.2 GiB)

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

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

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

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

    Code

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

    8. utils.py: helper functions and classes.


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