New publication on modeling music with word2vec in Springer's Neural Computing and Applications
Together with Prof Ching-Hua Chuan from the University of Miami and Prof. Kat Agres from IHPC, A*STAR, I've just published a new article on 'From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec', in Springer's Neural Computing and Applications (impact factor 4.213). The article describes how we can use word2vec to model complex polyphonic pieces of music using the popular embeddings model. The preprint is available here.
We explore the potential of a popular distributional semantics vector space model, word2vec, for capturing meaningful relationships in ecological (complex poly-phonic) 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