GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → posenhuang → Deeplearningsourceseparation

posenhuang / Deeplearningsourceseparation

Licence: other
Deep Recurrent Neural Networks for Source Separation

Programming Languages

matlab
3953 projects

Labels

deep-learningrnn

Projects that are alternatives of or similar to Deeplearningsourceseparation

LSTM-CTC-recaptcha
recaptcha with lstm and mxnet
Stars: ✭ 28 (-91.74%)
Mutual labels:  rnn
Musicgenerator
Experiment diverse Deep learning models for music generation with TensorFlow
Stars: ✭ 269 (-20.65%)
Mutual labels:  rnn
Sars tutorial
Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018
Stars: ✭ 320 (-5.6%)
Mutual labels:  rnn
EdgarAllanPoetry
Computer-generated poetry
Stars: ✭ 22 (-93.51%)
Mutual labels:  rnn
Lstm Human Activity Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
Stars: ✭ 2,943 (+768.14%)
Mutual labels:  rnn
Mydeeplearning
A deep learning library to provide algs in pure Numpy or Tensorflow.
Stars: ✭ 281 (-17.11%)
Mutual labels:  rnn
Pytorch-POS-Tagger
Part-of-Speech Tagger and custom implementations of LSTM, GRU and Vanilla RNN
Stars: ✭ 24 (-92.92%)
Mutual labels:  rnn
Basicocr
BasicOCR是一个致力于解决自然场景文字识别算法研究的项目。该项目由长城数字大数据应用技术研究院佟派AI团队发起和维护。
Stars: ✭ 336 (-0.88%)
Mutual labels:  rnn
Deeplearning.ai Assignments
Stars: ✭ 268 (-20.94%)
Mutual labels:  rnn
Neural Symbolic Machines
Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.
Stars: ✭ 305 (-10.03%)
Mutual labels:  rnn
deep-learning-coursera-complete
Deep Learning Specialization by Andrew Ng on Coursera - My Completed Coursework Repo - All 5 Courses
Stars: ✭ 104 (-69.32%)
Mutual labels:  rnn
Handwritingrecognitionsystem
Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture
Stars: ✭ 262 (-22.71%)
Mutual labels:  rnn
Bitcoinforecast
Predict bitcoin price with deep learning
Stars: ✭ 285 (-15.93%)
Mutual labels:  rnn
captioning chainer
A fast implementation of Neural Image Caption by Chainer
Stars: ✭ 17 (-94.99%)
Mutual labels:  rnn
R Net
A Tensorflow Implementation of R-net: Machine reading comprehension with self matching networks
Stars: ✭ 321 (-5.31%)
Mutual labels:  rnn
sgrnn
Tensorflow implementation of Synthetic Gradient for RNN (LSTM)
Stars: ✭ 40 (-88.2%)
Mutual labels:  rnn
Rnnsharp
RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.
Stars: ✭ 277 (-18.29%)
Mutual labels:  rnn
Text Classification Cnn Rnn
CNN-RNN中文文本分类，基于TensorFlow
Stars: ✭ 3,613 (+965.78%)
Mutual labels:  rnn
Tensorflow poems
中文古诗自动作诗机器人，屌炸天，基于tensorflow1.10 api，正在积极维护升级中，快star，保持更新！
Stars: ✭ 3,429 (+911.5%)
Mutual labels:  rnn
Unet Zoo
A collection of UNet and hybrid architectures in PyTorch for 2D and 3D Biomedical Image segmentation
Stars: ✭ 302 (-10.91%)
Mutual labels:  rnn
View All Similar Projects ➔

Deep Learning For Monaural Source Separation

Demo

Webpage: https://sites.google.com/site/deeplearningsourceseparation/

Experiments

MIR-1K experiment (singing voice separation)

  1. Training code: codes/mir1k/train_mir1k_demo.m

  2. Demo

  • Download a trained model http://www.ifp.illinois.edu/~huang146/DNN_separation/model_400.mat
  • Put the model at codes/mir1k/demo and go to the folder
  • Run: codes/mir1k/demo/run_test_single_model.m

TIMIT experiment (speech separation)

  1. Training code: codes/timit/train_timit_demo.m and codes/timit/train_timit_demo_mini_clip.m

  2. Demo

  • Download a trained model http://www.ifp.illinois.edu/~huang146/DNN_separation/timit_model_70.mat
  • Put the model at codes/timit/demo and go to the folder
  • Run: codes/timit/demo/run_test_single_model.m

TSP experiment (speech separation)

  1. Training code: codes/TSP/train_TSP_demo_mini_clip.m

  2. Demo

  • Download a trained model http://www.ifp.illinois.edu/~huang146/DNN_separation/TSP_model_RNN1_win1_h300_l2_r0_64ms_1000000_softabs_linearout_RELU_logmel_trn0_c1e-10_c0.001_bsz100000_miter10_bf50_c0_d0_7650.mat
  • Put the model at codes/TSP/demo and go to the folder
  • Run the demo code at codes/TSP/demo/run_test_single_model.m

Denosing experiment

  1. Put original FCJF0, FDAW0', FDML0, FECD0, 'FETB0', 'FJSP0', 'FKFB0', 'FMEM0', 'FSAH0', 'FSJK1', 'FSMA0', 'FTBR0', 'FVFB0' 'FVMH0 of the original TIMIT data under codes/denoising/Data/timit/

  2. Training code: codes/denoising/train_denoising_demo.m

  3. Demo

  • Download a trained model http://www.ifp.illinois.edu/~huang146/DNN_separation/denoising_model_870.mat
  • Put the model at codes/denoising/demo and go to the folder
  • Run the demo code at codes/denoising/demo/run_test_single_model.m

Dependencies

  1. The package is modified based on rnn-speech-denoising

  2. The software depends on Mark Schmidt's minFunc package for convex optimization.

  3. Additionally, we have included Mark Hasegawa-Johnson's HTK write and read functions that are used to handle the MFCC files.

  4. We use HTK for computing features (MFCC, logmel) (HCopy).

  5. We use signal processing functions from labrosa.

  6. We use BSS Eval toolbox Version 2.0, 3.0 for evaluation.

  7. We use MIR-1K for singing voice separation task.

  8. We use TSP for speech separation task.

Work on your data:

  1. To try the codes on your data, see mir1k, TSP settings - put your data into codes/mir1k/Wavfile or codes/TSP/Data/ accordingly.

  2. Look at the unit test parameters below codes/mir1k/train_mir1k_demo.m, codes/TSP/train_TSP_demo_mini_clip.m (with minibatch lbfgs, gradient clipping)

  3. Tune the parameters on the dev set and check the results.

Reference

  1. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation", IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 12, pp. 2136–2147, Dec. 2015

  2. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "Singing-Voice Separation From Monaural Recordings Using Deep Recurrent Neural Networks," in International Society for Music Information Retrieval Conference (ISMIR) 2014.

  3. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, P. Smaragdis, "Deep Learning for Monaural Speech Separation," in IEEE International Conference on Acoustic, Speech and Signal Processing 2014.

Notes

The codes are tested using MATLAB R2015a

Related Implementations

source_separaton_ml_jeju

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].