Learning and Evaluating Musical Features with Deep Autoencoders

06/14/2017

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by   Mason Bretan, 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.