Learning and Evaluating Musical Features with Deep Autoencoders

06/14/2017
by   Mason Bretan, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset