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From context to concept: exploring semantic relationships in music with word2vec

Abstract

We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex polyphonic) music. More precisely, the skip-gram version of word2vec is used to model slices of music from a large corpus spanning eight musical genres. In this newly learned vector space, a metric based on cosine distance is able to distinguish between functional chord relationships, as well as harmonic associations in the music. Evidence, based on cosine distance between chord-pair vectors, suggests that an implicit circle-of-fifths exists in the vector space. In addition, a comparison between pieces in different keys reveals that key relationships are represented in word2vec space. These results suggest that the newly learned embedded vector representation does in fact capture tonal and harmonic characteristics of music, without receiving explicit information about the musical content of the constituent slices. In order to investigate whether proximity in the discovered space of embeddings is indicative of ‘semantically-related’ slices, we explore a music generation task, by automatically replacing existing slices from a given piece of music with new slices. We propose an algorithm to find substitute slices based on spatial proximity and the pitch class distribution inferred in the chosen subspace. The results indicate that the size of the subspace used has a significant effect on whether slices belonging to the same key are selected. In sum, the proposed word2vec model is able to learn music-vector embeddings that capture meaningful tonal and harmonic relationships in music, thereby providing a useful tool for exploring musical properties and comparisons across pieces, as a potential input representation for deep learning models, and as a music generation device.

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Notes

  1. http://musedata.org.

  2. http://piano-midi.de.

  3. http://ifdo.ca/seymour/nottingham/nottingham.html.

  4. https://www.reddit.com/r/datasets/comments/3akhxy/the_largest_midi_collection_on_the_internet.

  5. http://stoneyroads.com/2015/06/behold-the-worlds-biggest-midi-collection-on-the-internet/.

  6. www.jukedeck.com/.

  7. http://sites.google.com/view/chinghuachuan/.

  8. https://sites.google.com/view/chinghuachuan/.

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Acknowledgements

This research was partly supported through SUTD Grant No. SRG ISTD 2017 129.

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Correspondence to Ching-Hua Chuan.

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Appendix

Appendix

See Fig. 10.

Fig. 10
figure 10

Generated slices and their cosine distance to the original slices from Chopin’s Mazurka Op. 67 No. 4, using a top 1, b top 5, c top 10, and d top 20 slices for the search in music word2vec space. Note that as the value of n increases (e.g., moving from figure a down to d), the number of pitches outside of the key (see generated pitches in black) decreases

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Chuan, CH., Agres, K. & Herremans, D. From context to concept: exploring semantic relationships in music with word2vec. Neural Comput & Applic 32, 1023–1036 (2020). https://doi.org/10.1007/s00521-018-3923-1

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Keywords

  • Word2vec
  • Music
  • Semantic vector space model