Pinned Repositories
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
annotated-transformer
An annotated implementation of the Transformer paper.
Audio-Noise-Reduction
auraloss
Collection of audio-focused loss functions in PyTorch
bryan-pardo.github.io
A truly simple website template for academics
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.
GloveTutorial
All of the code for my Medium articles
mdx-submissions21
Music Demixing Challenge Submission Repo
pytorch-unet
Simple PyTorch implementations of U-Net/FullyConvNet (FCN) for image segmentation
vampnet
music generation with masked transformers!
bryan-pardo's Repositories
bryan-pardo/auraloss
Collection of audio-focused loss functions in PyTorch
bryan-pardo/academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
bryan-pardo/annotated-transformer
An annotated implementation of the Transformer paper.
bryan-pardo/Audio-Noise-Reduction
bryan-pardo/bryan-pardo.github.io
A truly simple website template for academics
bryan-pardo/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.
bryan-pardo/GloveTutorial
All of the code for my Medium articles
bryan-pardo/mdx-submissions21
Music Demixing Challenge Submission Repo
bryan-pardo/pytorch-unet
Simple PyTorch implementations of U-Net/FullyConvNet (FCN) for image segmentation
bryan-pardo/vampnet
music generation with masked transformers!
bryan-pardo/Wave-U-Net-for-Speech-Enhancement
Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.
bryan-pardo/Wave-U-Net-Pytorch
Improved Wave-U-Net implemented in Pytorch