musicgenerationvae

由 A Roberts 著作 · 2018 · 被引用 538 次 — The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data.

MusicVAE. Attempting to recreate a Hierarchical Variational Autoencoder for Music in PyTorch. This is a project done during the course 02456 Deep Learning ...

It can be used to generate new music and to interpolate between existing or randomly generated music sequences with a command line interface (CLI). Furthermore, ...

由 T Wang 著作 · 2020 · 被引用 9 次 — In this paper, GAN and VAE are combined with deep learning network to generate intelligent music based on music theory rules, and to explore ...

由 IP Yamshchikov 著作 · 2020 · 被引用 41 次 — A VAE makes assumptions concerning the distribution of latent variables and applies a variational approach for latent representation learning.

... Music, Adam Roberts, Jesse Engel ... ○ Generate a continuous transition of music generation on the latent space ... ○ https://magenta.tensorflow.org/music-vae.

2018年3月15日 — When a painter creates a work of art, she first blends and explores color options on an artist's palette before applying them to the canvas.

VAE is an autoencoder whose encodings distribution is regularised during the training in order to ensure that its latent space has good properties allowing us ...

2023年12月8日 — In AI music generation, Variational Autoencoders (VAEs) are revolutionary. VAEs are a class of generative models in machine learning that ...