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POP909 Dataset for Music Arrangement Generation

This is the dataset repository for the paper: POP909: A Pop-song Dataset for Music Arrangement Generation, in ISMIR 2020.

Dataset Zip File Structure

  • index.xlsx: it contains a list describing the baisc information of each index folder/file (name, number of beats per measure, number of quavers per measure, and modify times)

  • index folder: it contains several files for a data in the POP909 dataset:

  • index.mid: the music midi file of the arrangement song (MELODY track for the main melody, BRIDGE track for the sub-melody, and PIANO track for the accompaniment)

  • beat_audio/beat_midi.txt: the extracted beat information from the raw audio/midi, the first column is the time (in sec), and the seconcd column is the beat order

  • chord_audio/beat_audio.txt: the extracted chord information from the raw audio/midi, the first/second column is the start/end time (in sec), and the third column is the chord name

  • key_audio.txt: the extracted key change information from the raw audio, the first/second column is the start/end time (in sec), and the third column is the key change.

  • versions folder: it contains different versions of the same arrangement song.

Data Processing Script

We also provide scripts for the data processing. It will allow you to quickly process the POP909 Files (Midi) into the Google Magenta's music representation as like Music Transformer and Performance RNN.

  • data_process.ipynb: follow this jupyter notebook, you will get the data input tokens that are able to be fed into the pytorch/tensorflow dataset/dataloader. (Notice that the representation of encoding the midi sequence are various {e.g., monophonic note tokens, magenta's event tokens, pianoroll, etcs}. We highly recommend users to create their own data processing files to encode the data in their wanted format)
  • pop-pickle.zip: it contains the pickle file, already in magenta's event tokens representation

Credit

Please cite this work if you want to use this dataset

@inproceedings{pop909-ismir2020,
    author = {Ziyu Wang* and Ke Chen* and Junyan Jiang and Yiyi Zhang and Maoran Xu and Shuqi Dai and Guxian Bin and Gus Xia},
    title = {POP909: A Pop-song Dataset for Music Arrangement Generation},
    booktitle = {Proceedings of 21st International Conference on Music Information Retrieval, {ISMIR}},
    year = {2020}
}