Joly2001

Joly2001's Stars

• xiph/rnnoise

  Recurrent neural network for audio noise reduction

  Language:C4k888

• sthenc/bss_tester

  Python scripts for generating test examples for blind source separation. Developed on Linux, using python.

  Language:Python41

• dpwe/casapy

  Audio source separation using CASA approaches in Python.

  Language:Python113

• google-research/sound-separation

  Language:Python643117

• bytedance/music_source_separation

  Language:Python1.3k194

• deezer/spleeter

  Deezer source separation library including pretrained models.

  Language:Python25.7k2.8k

• bill9800/Speech-denoise-Autoencoder

  Speech denoiser model using Keras

  Language:Python2011

• drethage/speech-denoising-wavenet

  A neural network for end-to-end speech denoising

  Language:Python672164

• vbelz/Speech-enhancement

  Deep learning for audio denoising

  Language:Python646124

• yuzhou-git/deep-casa

  Tensorflow implementation of deep CASA

  Language:Python6212

• musikalkemist/praudio

  Audio preprocessing framework for Deep Learning audio applications

  Language:Python12310

• yeyupiaoling/AudioClassification-PaddlePaddle

  基于PaddlePaddle实现的音频分类，支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型，还有多种预处理方法

  Language:Python8514

• yeyupiaoling/AudioClassification-Tensorflow

  基于Tensorflow实现声音分类，博客地址：

  Language:Python9624

• ArlanCooper/Awesome_KnowlegeDistillation

  Awesome Resources of Knowledge Distillation. 从零开始知识蒸馏。

  6

• geyuying/PF-AFN

  Official code for "Parser-Free Virtual Try-on via Distilling Appearance Flows", CVPR 2021.

  Language:Python540137

• haitongli/knowledge-distillation-pytorch

  A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

  Language:Python1.8k342

• AberHu/Knowledge-Distillation-Zoo

  Pytorch implementation of various Knowledge Distillation (KD) methods.

  Language:Python1.6k265

• JusperLee/Speech-Separation-Paper-Tutorial

  A must-read paper for speech separation based on neural networks

  744137

• 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

• etzinis/two_step_mask_learning

  A two step optimization for sound source separation on the adaptive front-end domain

  Language:Python6616

• busyyang/python_sound_open

  语音信号处理试验教程，Python代码

  Language:Python310110

• kaituoxu/TasNet

  A PyTorch implementation of Time-domain Audio Separation Network (TasNet) with Permutation Invariant Training (PIT) for speech separation.

  Language:Python10931

• xuchenglin28/speech_separation

  Constrained Permutation Invariant Training, Speech Separation

  Language:Python4210

• zhr1201/deep-clustering

  A tensorflow implementation for Deep clustering: Discriminative embeddings for segmentation and separation

  Language:Python13570

• KrishnaDN/x-vector-pytorch

  Implementation of the paper "Spoken Language Recognition using X-vectors" in Pytorch

  Language:Python10425

• BUTSpeechFIT/x-vector-kaldi-tf

  Tensorflow implementation of x-vector topology on top of Kaldi recipe

  Language:Python11932

• castorini/honk

  PyTorch implementations of neural network models for keyword spotting

  Language:Python510124

• ben1234560/AiLearning-Theory-Applying

  快速上手AI理论及应用实战：基础知识、Transformer、NLP、ML、DL、竞赛。含大量注释及数据集，力求每一位能看懂并复现。

  Language:Jupyter Notebook3k435