JMCheng-SEU

JMCheng-SEU's Stars

• Jamie-Stirling/RetNet

  An implementation of "Retentive Network: A Successor to Transformer for Large Language Models"

  Language:Python1.2k101

• BUTSpeechFIT/torch_msbg_mbstoi

  Differentiable implementation of MSBG hearing loss model and MBSTOI intelligibility metric for Clarity Enhancement challenge.

  Language:Python164

• Enny1991/beamformers

  Easy to use Beamformers for multi-channel speech separation/enhancement

  Language:Python19049

• RicherMans/GPV

  Repository for our Interspeech2020 general-purpose voice activity detection (GPVAD) paper

  Language:Python14229

• zehuachenImperial/SkipConvNet

  Speech Dereverberation using Fully Convolutional Networks

  6944

• facebookresearch/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.

  Language:Python1.7k302

• Zihang97/PAGAN

  PAGAN: a phase-adapted GAN for speech enhancement

  Language:Python349

• anicolson/DeepXi

  Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

  Language:MATLAB502127

• funcwj/setk

  Tools for Speech Enhancement integrated with Kaldi

  Language:Python40492

• microsoft/P.808

  This is an open-source implementation of the ITU P.808 standard for "Subjective evaluation of speech quality with a crowdsourcing approach" (see https://www.itu.int/rec/T-REC-P.808/en). It uses Amazon Mechanical Turk as the crowdsourcing platform. It includes implementations for Absolute Category Rating (ACR), Degradation Category Rating (DCR), and Comparison Category Rating (CCR).

  Language:HTML21258