2624428587

2624428587's Stars

• yxlu-0102/MP-SENet

  Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

  Language:Python28745

• caoruitju/RUI_SE

  VOICOR: A Residual Iterative Voice Correction Framework for Monaural Speech Enhancement

  Language:Python388

• sweetcocoa/DeepComplexUNetPyTorch

  Implementation of Deep Complex UNet Using PyTorch

  Language:Python15039

• ShiArthur03/ShiArthur03

  Language:MATLAB10.4k1.9k

• snuhcs/Papez

  Papez: Resource-Efficient Speech Separation with Auditory Working Memory (ICASSP 2023)

  Language:Python19

• pragyak412/Improving-Voice-Separation-by-Incorporating-End-To-End-Speech-Recognition

  Implementing the paper -

  Language:Python182

• etzinis/fedenhance

  Code for the paper: Separate but togerher: Unsupervised Federated Learning for Speech Enhancement from non-iid data

  Language:Jupyter Notebook394

• sp-uhh/deep-non-linear-filter

  Language:Python4410

• Andong-Li-speech/EaBNet

  This is the repo of the manuscript "Embedding and Beamforming: All-Neural Causal Beamformer for Multichannel Speech Enhancement", which was submitted to ICASSP2022.

  Language:Python7614

• jakugel/unet-variants

  Tensorflow 2 code for several U-Net variants to perform direct comparisons including base, attention, dense, ++, squeeze-excite, inception, residual, recurrent-residual

  Language:Python123

• Audio-WestlakeU/McNet

  The official repo: "McNet: Fuse Multiple Cues for Multichannel Speech Enhancement", ICASSP 2023

  Language:Python10113

• microsoft/DNS-Challenge

  This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.

  Language:Python1.1k409

• ModarHalimeh/COSPA

  Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement

  Language:Python3012

• Audio-WestlakeU/NBSS

  The official repo of NBC & SpatialNet for multichannel speech separation, denoising, and dereverberation

  Language:Python19824

• facebookresearch/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.

  Language:Python1.2k177

• f90/Wave-U-Net

  Implementation of the Wave-U-Net for audio source separation

  Language:Python824177

• Audio-WestlakeU/FullSubNet

  PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

  Language:Python536153

• ujscjj/DPTNet

  Language:Python10524

• wenet-e2e/wenet

  Production First and Production Ready End-to-End Speech Recognition Toolkit

  Language:Python4.1k1.1k

• XiangzhuKong/CA-Dense-UNet

  An unofficial code reproduction of Channel Attention Dense U-Net for Multichannel Speech Enhancement

  Language:Python111

• felixfuyihui/Uformer

  Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation

  Language:Python9415

• jwr1995/DTCN

  Language:Python142

• YUCHEN005/Unified-Enhance-Separation

  Code for paper "Unifying Speech Enhancement and Separation with Gradient Modulation for End-to-End Noise-Robust Speech Separation"

  Language:Python367

• aleXiehta/WaveCRN

  WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement

  Language:Jupyter Notebook3510

• ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation

  A PyTorch implementation of dual-path RNNs (DPRNNs) based speech separation described in "Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation".

  Language:Python16641

• jyhan03/icassp22-dataset

  Dataset simulation for DPCCN.

  Language:Shell142

• Sinica-SLAM/CasNet

  2

• maggie0830/DCCRN

  implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" by pytorch

  18031

• alibabasglab/FRCRN

  12712

• ododoyo/EHNet

  A neural network consist of cnn and lstm for speech enhancement

  Language:Python2410