Pinned Repositories
Conv-TasNet
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
Conv-TasNet-1
demucs
Code for the paper Music Source Separation in the Waveform Domain
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.
Noise-Estimation-for-Generative-Diffusion-Models
Non-Gaussian-Denoising-Diffusion-Models
speaker_separation
speaker_separation
Unsupervised_Singing_Voice_Conversion
HyperNetworkDecoder
Hyper Graph Network Decoders for Block Codes
svoice
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
enk100's Repositories
enk100/speaker_separation
speaker_separation
enk100/Unsupervised_Singing_Voice_Conversion
enk100/Non-Gaussian-Denoising-Diffusion-Models
enk100/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.
enk100/Noise-Estimation-for-Generative-Diffusion-Models
enk100/Conv-TasNet
A PyTorch implementation of Conv-TasNet described in "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation" with Permutation Invariant Training (PIT).
enk100/Conv-TasNet-1
enk100/demucs
Code for the paper Music Source Separation in the Waveform Domain
enk100/Hyper-Graph-Network-Decoders-for-Block-Codes
Hyper-Graph-Network Decoders for Block Codes
enk100/img2midi
Converts image files to midi songs
enk100/OpenShadingLanguage
Advanced shading language for production GI renderers
enk100/polyglot
enk100/SimulTron