Resynthesis
12 papers with code • 2 benchmarks • 2 datasets
LibriSpeech
LJSpeech
Most implemented papers
Detecting the Unexpected via Image Resynthesis
cvlab-epfl/detecting-the-unexpected
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ICCV 2019
In this paper, we tackle the more realistic scenario where unexpected objects of unknown classes can appear at test time.
Differentiable Time-Frequency Scattering on GPU
cyrusvahidi/kymatio-wavespin
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Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales.
Generative Spoken Language Modeling from Raw Audio
pytorch/fairseq
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We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation.
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
facebookresearch/speech-resynthesis
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We propose using self-supervised discrete representations for the task of speech resynthesis.
Unifying Probabilistic Models for Time-Frequency Analysis
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.
Coordinate-based Texture Inpainting for Pose-Guided Image Generation
dolorousrtur/coordinate_based_inpainting
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Since the input photograph always observes only a part of the surface, we suggest a new inpainting method that completes the texture of the human body.
On Adversarial Mixup Resynthesis
christopher-beckham/amr
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NeurIPS 2019
In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders.
Parametric Resynthesis with neural vocoders
We propose to utilize the high quality speech generation capability of neural vocoders for noise suppression.
Spectral Processing of COVID-19 Time-Series Data
The presence of oscillations in aggregated COVID-19 data not only raises questions about the data's accuracy, it hinders understanding of the pandemic.
Dynamical Variational Autoencoders: A Comprehensive Review
Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models.
