Learning and Evaluating Musical Features with Deep Autoencoders
This work addresses feature learning for music analysis, but appears incremental as it applies existing autoencoder techniques to a specific domain.
The paper tackled the problem of learning musical embeddings from symbolic representations using autoencoding methods, and evaluated them on prediction and classification tasks, but did not report specific numerical results.
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.