Eliya Nachmani's repositories
speaker_separation
speaker_separation
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.
Conv-TasNet
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
demucs
Code for the paper Music Source Separation in the Waveform Domain
Hyper-Graph-Network-Decoders-for-Block-Codes
Hyper-Graph-Network Decoders for Block Codes
OpenShadingLanguage
Advanced shading language for production GI renderers