Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Capturing Musical Structure Using Convolutional Recurrent Latent Variable Model
Eunjeong Koh, Dustin Wright, Shlomo Dubnov
Feb 12, 2018 (modified: Jun 04, 2018)ICLR 2018 Workshop Submissionreaders: everyoneShow Bibtex
Abstract:In this paper, we present a model for learning musical features and generating novel sequences of music. Our model, the Convolutional-Recurrent Variational Autoencoder (C-RVAE), captures short-term polyphonic sequential musical structure using a Convolutional Neural Network as a front-end. To generate sequential data, we apply the recurrent latent variational model, which uses an encoder-decoder architecture with latent probabilistic connections to capture the hidden structure of music. Using the sequence-to-sequence model, our generative model can exploit samples from a prior distribution and generate a longer sequence of music.
Keywords:variational autoencoder, convolutional neural networks, recurrent neural networks, deep learning for music
TL;DR:We explore the Convolutional-Recurrent Variational Autoencoder (C-RVAE), which is an effective method of learning useful musical features that we use for polyphonic music generation.
Enter your feedback below and we'll get back to you as soon as possible.