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Found 10 papers, 8 papers with code
VampNet: Music Generation via Masked Acoustic Token Modeling
We introduce VampNet, a masked acoustic token modeling approach to music synthesis, compression, inpainting, and variation.
High-Fidelity Audio Compression with Improved RVQGAN
Language models have been successfully used to model natural signals, such as images, speech, and music.
Chunked Autoregressive GAN for Conditional Waveform Synthesis
We show that simple pitch and periodicity conditioning is insufficient for reducing this error relative to using autoregression.
NU-GAN: High resolution neural upsampling with GAN
In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling).
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques.
EnGAN: Latent Space MCMC and Maximum Entropy Generators for Energy-based Models
Unsupervised learning is about capturing dependencies between variables and is driven by the contrast between the probable vs improbable configurations of these variables, often either via a generative model which only samples probable ones or with an energy function (unnormalized log-density) which is low for probable ones and high for improbable ones.
Maximum Entropy Generators for Energy-Based Models
Maximum likelihood estimation of energy-based models is a challenging problem due to the intractability of the log-likelihood gradient.
Harmonic Recomposition using Conditional Autoregressive Modeling
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017).
ObamaNet: Photo-realistic lip-sync from text
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time.
