metadata

dataset_info:
  features:
    - name: token
      dtype: string
    - name: content
      dtype: string
    - name: vector
      sequence: float32
  splits:
    - name: tag
      num_bytes: 2740380
      num_examples: 2227
    - name: artist
      num_bytes: 46025354
      num_examples: 37002
    - name: track
      num_bytes: 898952880
      num_examples: 697812
  download_size: 1387722409
  dataset_size: 947718614
configs:
  - config_name: default
    data_files:
      - split: tag
        path: data/tag-*
      - split: artist
        path: data/artist-*
      - split: track
        path: data/track-*
tags:
  - music

Musical Word Embedding

Musical Word Embedding for Music Tagging and Retrieval

SeungHeon Doh, Jongpil Lee, Dasaem Jeong, Juhan Nam
To appear IEEE Transactions on Audio, Speech and Language Processing (submitted)

Word embedding has become an essential means for text-based information retrieval. Typically, word embeddings are learned from large quantities of general and unstructured text data. However, in the domain of music, the word embedding may have difficulty understanding musical contexts or recognizing music-related entities like artists and tracks. To address this issue, we propose a new approach called Musical Word Embedding (MWE), which involves learning from various types of texts, including both everyday and music-related vocabulary.

Resources: Using Musical Word Embedding

Run the download script for embedding vector:

Check our huggingface dataset: You can download important embedding vectors such as tag, artist, and track from the Hugging Face dataset.

from datasets import load_dataset
dataset = load_dataset("seungheondoh/musical-word-embedding")

{
  "token": "happy", 
  "content": "happy",
  "vector": [0.011484057642519474, -0.07818693667650223, -0.02778349258005619, 0.052311971783638, -0.1324823945760727, 0.03757447376847267, 0.007125925272703171, ...]
  },{
  "token": "ARYZTJS1187B98C555", 
  "content": "Faster Pussycat",
  "vector": [-0.13004058599472046, -1.3509420156478882, -0.3012666404247284, -0.34076201915740967, -0.8142353296279907, 0.3902665972709656, -0.1903497576713562, 0.6163021922111511, ...]
}

For other general 10M word vectors, you can also download them using the script below.

bash scripts/download.sh

Citation

If you find this work useful, please cite it as:

@article{doh2024musical,
  title={Musical Word Embedding for Music Tagging and Retrieval},
  author={Doh, SeungHeon and Lee, Jongpil and Jeong, Dasaem and Nam, Juhan},
  journal={update_soon},
  year={2024}
}

@inproceedings{doh2021million,
  title={Million song search: Web interface for semantic music search using musical word embedding},
  author={Doh, S and Lee, Jongpil and Nam, Juhan},
  booktitle={International Society for Music Information Retrieval Conference, ISMIR},
  year={2021}
}

@article{doh2020musical,
  title={Musical word embedding: Bridging the gap between listening contexts and music},
  author={Doh, Seungheon and Lee, Jongpil and Park, Tae Hong and Nam, Juhan},
  journal={arXiv preprint arXiv:2008.01190},
  year={2020}
}

Feel free to reach out for any questions or feedback!