Learning and Evaluating Musical Features with Deep Autoencoders

Mason Bretan, Sageev Oore, Doug Eck, Larry Heck

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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