Pinned Repositories
Best-README-Template
An awesome README template to jumpstart your projects!
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.
final_project_EDP
Final project, Experimantal Data Processing course, 1st Term
final_project_ML
hifi-gan
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
hyperbolic-image-embeddings
Supplementary code for the paper "Hyperbolic Image Embeddings".
Morpheus
Contextual Lemmatization and Morphological Tagging in 100 different languages. A Participant System for SigMorphon2019 Task 2
NLA_project
SincNet
SincNet is a neural architecture for efficiently processing raw audio samples.
stylish-fastmri
NicholasBabaev's Repositories
NicholasBabaev/Best-README-Template
An awesome README template to jumpstart your projects!
NicholasBabaev/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.
NicholasBabaev/final_project_EDP
Final project, Experimantal Data Processing course, 1st Term
NicholasBabaev/final_project_ML
NicholasBabaev/hifi-gan
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
NicholasBabaev/hyperbolic-image-embeddings
Supplementary code for the paper "Hyperbolic Image Embeddings".
NicholasBabaev/Morpheus
Contextual Lemmatization and Morphological Tagging in 100 different languages. A Participant System for SigMorphon2019 Task 2
NicholasBabaev/NLA_project
NicholasBabaev/SincNet
SincNet is a neural architecture for efficiently processing raw audio samples.
NicholasBabaev/stylish-fastmri