README.md

metadata

license: apache-2.0
dataset_info:
  features:
    - name: id
      dtype: int32
    - name: filename
      dtype: string
    - name: x
      dtype:
        audio:
          sampling_rate: 16000
    - name: instrument
      dtype:
        class_label:
          names:
            '0': flute
            '1': saxophone
            '2': trumpet
            '3': violin
    - name: tonic
      dtype:
        class_label:
          names:
            '0': A
            '1': A#
            '2': B
            '3': C
            '4': C#
            '5': D
            '6': D#
            '7': E
            '8': F
            '9': F#
            '10': G
            '11': G#
    - name: octave
      dtype:
        class_label:
          names:
            '0': '4'
    - name: scale
      dtype:
        class_label:
          names:
            '0': blues
            '1': major
            '2': minor
    - name: rhythm_bar1
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'
            '2': '2'
            '3': '3'
            '4': '4'
            '5': '5'
            '6': '6'
            '7': '7'
            '8': '8'
            '9': '9'
            '10': '10'
            '11': '11'
            '12': '12'
            '13': '13'
            '14': '14'
            '15': '15'
            '16': '16'
            '17': '17'
            '18': '18'
            '19': '19'
            '20': '20'
            '21': '21'
            '22': '22'
            '23': '23'
            '24': '24'
            '25': '25'
            '26': '26'
            '27': '27'
    - name: arp_chord1
      dtype:
        class_label:
          names:
            '0': down
            '1': up
    - name: arp_chord2
      dtype:
        class_label:
          names:
            '0': down
            '1': up
  splits:
    - name: test
      num_bytes: 227141023
      num_examples: 2420
    - name: train
      num_bytes: 1060220348.535
      num_examples: 11289
    - name: val
      num_bytes: 226830467.066
      num_examples: 2419
  download_size: 1422650313
  dataset_size: 1514191838.6009998
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*

MSD dMelodies-WAV Dataset Attribution

The Multi-factor Sequential Disentanglement benchmark includes the dMelodies-WAV dataset, a synthetically generated collection of 48,000-sample audio waveforms labeled with one static factor (instrument) and five dynamic musical attributes. Extending the symbolic dMelodies benchmark into the raw audio domain, dMelodies-WAV was created by synthesizing a subset of dMelodies using the MIDI-DDSP neural audio synthesis model, producing realistic audio across four instruments. This dataset provides both global factors (instrument, tonic, mode) and local factors (rhythm, arpeggiation), making it a strong disentanglement benchmark for studying global-local and hierarchical factor models in raw waveform music.

• Original repositories:

  https://github.com/ashispati/dmelodies_dataset

  https://github.com/magenta/midi-ddsp

• Reference papers:
  A. Pati, S. Gururani, A. Lerch.
  dMelodies: A Music Dataset for Disentanglement Learning, ISMIR 2020.
  https://arxiv.org/abs/2007.15067

  @inproceedings{pati2020dmelodies,
    title={dMelodies: A Music Dataset for Disentanglement Learning},
    author={Pati, Ashis and Gururani, Siddharth and Lerch, Alexander},
    booktitle={21st International Society for Music Information Retrieval Conference (ISMIR)},
    year={2020},
    address={Montréal, Canada}
  }
  

  Y. Wu, E. Manilow, Y. Deng, R. Swavely, K. Kastner, T. Cooijmans, A. Courville, C. Auang, J, Engel MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling, ISMIR 2021.
  https://arxiv.org/abs/2112.09312

  @inproceedings{
  wu2022mididdsp,
  title={{MIDI}-{DDSP}: Detailed Control of Musical Performance via Hierarchical Modeling},
  author={Yusong Wu and Ethan Manilow and Yi Deng and Rigel Swavely and Kyle Kastner and Tim Cooijmans and Aaron Courville and Cheng-Zhi Anna Huang and Jesse Engel},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=UseMOjWENv}
  }
  

⚠ Note: The dMelodies-WAV dataset is provided for non-commercial research purposes. Please cite the above when using this dataset in your work.