tavihalperin

AV-snap

AV-sync

Python implementation of the paper " Dynamic Temporal Alignment of Speech to Lips"

coremltools

Core ML Community Tools.

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

facial_attributes

indexnet_matting

Indices Matter: Learning to Index for Deep Image Matting

Neural-Egg-Seperation

Implementation of the paper "Neural separation of observed and unobserved distributions"

sky-optimization

Repository and website for Sky Optimization: Semantically aware image processing of skies in low-light photography

tavihalperin/AV-sync

Python implementation of the paper " Dynamic Temporal Alignment of Speech to Lips"

tavihalperin/Neural-Egg-Seperation

Implementation of the paper "Neural separation of observed and unobserved distributions"

tavihalperin/sky-optimization

Repository and website for Sky Optimization: Semantically aware image processing of skies in low-light photography

tavihalperin/AV-snap

tavihalperin/denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.

tavihalperin/coremltools

Core ML Community Tools.

tavihalperin/facial_attributes

tavihalperin/indexnet_matting

Indices Matter: Learning to Index for Deep Image Matting

tavihalperin/models

Models and examples built with TensorFlow

tavihalperin/planercnn

PlaneRCNN detects and reconstructs piece-wise planar surfaces from a single RGB image

tavihalperin/PWC-Net

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)

tavihalperin/pytorch-CycleGAN-and-pix2pix

Image-to-Image Translation in PyTorch