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2017

Learning and Evaluating Musical Features with Deep Autoencoders

Bretan, Mason, Oore, Sageev, Eck, Doug et al.

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In this work we describe and evaluate methods to learn musical embeddings.

Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic representation.
We consider autoencoding-based methods including denoising autoencoders, and context reconstruction, and evaluate the resulting embeddings on a forward prediction and a classification task.

Learning and Evaluating Musical Features with Deep Autoencoders · Around

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Comparison between language and music
Mireille Besson and Daniele Schön · 2001
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Music type classification by spectral contrast feature
Dan-Ning Jiang, Lie Lu, Hong-Jiang Zhang, Jian-Hua Tao, and Lian-Hong Cai · 2002
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A comparative study on content-based music genre classification
Tao Li, Mitsunori Ogihara, and Qi Li · 2003
Earlier work this paper cites.
Semantic hashing
R. Salakhutdinov and G Hinton · 2007
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