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arXiv · 1706.04486

Learning and Evaluating Musical Features with Deep Autoencoders

Mason Bretan·Sageev Oore·Doug Eck·Larry Heck
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Abstract

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.

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Mason Bretan, Sageev Oore, Doug Eck, Larry Heck. 2017-06-14. Learning and Evaluating Musical Features with Deep Autoencoders. https://arxiv.org/abs/1706.04486

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