Kundan Kumar

TL;DR: The model is non-autoregressive, fully convolutional, with significantly fewer parameters than competing models and generalizes to unseen speakers for mel-spectrogram inversion, and suggests a set of guidelines to design general purpose discriminators and generators for conditional sequence synthesis tasks.

TL;DR: Char2Wav is an end-to-end model for speech synthesis that learns to produce audio directly from text and is a bidirectional recurrent neural network with attention that produces vocoder acoustic features.

TL;DR: It is shown that the model, which profits from combining memory-less modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure is able to capture underlying sources of variations in the temporal sequences over very long time spans, on three datasets of different nature.

TL;DR: In this article, the authors proposed a novel model for unconditional audio generation based on generating one audio sample at a time, which profits from combining memoryless modules, namely autoregressive multilayer perceptrons, and stateful recurrent neural networks in a hierarchical structure.

TL;DR: PixelVAE as mentioned in this paper is a VAE model with an autoregressive decoder based on PixelCNN, which achieves state-of-the-art performance on binarized MNIST, competitive performance on 64x64 ImageNet, and high quality samples on the LSUN bedrooms dataset.