@article {9998,
	title = {From Context to Concept: Exploring Semantic Relationships in Music with Word2Vec},
	journal = {Neural Computing and Applications},
	volume = {https://doi.org/10.1007/s00521-018-3923-1},
	year = {2018},
	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 {\textquoteleft}semantically-related{\textquoteright} 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.},
	keywords = { semantic vector space model, music, word2vec},
	author = {Chuan, C-H and Agres, K and Herremans, D}
}
@proceedings {2017,
	title = {From Bach to the Beatles: The simulation of human tonal expectation using ecologically-trained predictive models},
	year = {2017},
	address = {Suzhou, China},
	author = {Carlos Cancino-Chacon and Maarten Grachten and Kat Agres}
}
@article {39,
	title = {From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation},
	journal = {Journal of Artificial General Intelligence},
	volume = {6},
	year = {2015},
	pages = {55-86},
	abstract = {We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.},
	keywords = {behavioural validation, computational creativity, concept discovery, conceptual spaces, distributional semantics},
	issn = {1946-0163},
	doi = {10.1515/jagi-2015-0004},
	url = {http://www.degruyter.com/view/j/jagi.2015.6.issue-1/jagi-2015-0004/jagi-2015-0004.xml?format=INT$\#$fn1},
	author = {Stephen McGregor and Kat Agres and Matthew Purver and Geraint A. Wiggins}
}
