Abstract
In this paper, we use pitch-class vector embeddings to study scale relationships between composers. Recent research in natural language processing (NLP) has used machine learning to derive vector representations-known as embeddings—for words based on their co-occurrence. Borrowing from NLP, we use the word2vec algorithm to encode windows of pitch-classes, or pitch-class vectors, of music. We show that these embeddings not only replicate the well-known theoretical circle of fifths, but can also capture stylistic nuances between composers’ use of scales.
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Chiu, M. (2022). Investigating Style with Scale Embeddings. In: Montiel, M., Agustín-Aquino, O.A., Gómez, F., Kastine, J., Lluis-Puebla, E., Milam, B. (eds) Mathematics and Computation in Music. MCM 2022. Lecture Notes in Computer Science(), vol 13267. Springer, Cham. https://doi.org/10.1007/978-3-031-07015-0_37
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DOI: https://doi.org/10.1007/978-3-031-07015-0_37
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