All Projects → Js-Mim → Mss_pytorch

Js-Mim / Mss_pytorch

Singing Voice Separation via Recurrent Inference and Skip-Filtering Connections - PyTorch Implementation. Demo:

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Mss pytorch

Rcnn Text Classification
Tensorflow Implementation of "Recurrent Convolutional Neural Network for Text Classification" (AAAI 2015)
Stars: ✭ 127 (-23.03%)
Mutual labels:  recurrent-neural-networks
Arc Pytorch
The first public PyTorch implementation of Attentive Recurrent Comparators
Stars: ✭ 147 (-10.91%)
Mutual labels:  recurrent-neural-networks
Brain.js
brain.js is a GPU accelerated library for Neural Networks written in JavaScript.
Stars: ✭ 12,358 (+7389.7%)
Mutual labels:  recurrent-neural-networks
Image Caption Generator
A neural network to generate captions for an image using CNN and RNN with BEAM Search.
Stars: ✭ 126 (-23.64%)
Mutual labels:  recurrent-neural-networks
Crypto Rnn
Learning the Enigma with Recurrent Neural Networks
Stars: ✭ 139 (-15.76%)
Mutual labels:  recurrent-neural-networks
Stock Price Predictor
This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.
Stars: ✭ 146 (-11.52%)
Mutual labels:  recurrent-neural-networks
Rnn From Scratch
Use tensorflow's tf.scan to build vanilla, GRU and LSTM RNNs
Stars: ✭ 123 (-25.45%)
Mutual labels:  recurrent-neural-networks
Hey Jetson
Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.
Stars: ✭ 161 (-2.42%)
Mutual labels:  recurrent-neural-networks
Image Caption Generator
[DEPRECATED] A Neural Network based generative model for captioning images using Tensorflow
Stars: ✭ 141 (-14.55%)
Mutual labels:  recurrent-neural-networks
Rnn lstm from scratch
How to build RNNs and LSTMs from scratch with NumPy.
Stars: ✭ 156 (-5.45%)
Mutual labels:  recurrent-neural-networks
Deep Learning With Pytorch Tutorials
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
Stars: ✭ 1,986 (+1103.64%)
Mutual labels:  recurrent-neural-networks
Document Classifier Lstm
A bidirectional LSTM with attention for multiclass/multilabel text classification.
Stars: ✭ 136 (-17.58%)
Mutual labels:  recurrent-neural-networks
Tfvos
Semi-Supervised Video Object Segmentation (VOS) with Tensorflow. Includes implementation of *MaskRNN: Instance Level Video Object Segmentation (NIPS 2017)* as part of the NIPS Paper Implementation Challenge.
Stars: ✭ 151 (-8.48%)
Mutual labels:  recurrent-neural-networks
Deep Lyrics
Lyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network
Stars: ✭ 127 (-23.03%)
Mutual labels:  recurrent-neural-networks
Keras Lmu
Keras implementation of Legendre Memory Units
Stars: ✭ 160 (-3.03%)
Mutual labels:  recurrent-neural-networks
Deepecg
ECG classification programs based on ML/DL methods
Stars: ✭ 124 (-24.85%)
Mutual labels:  recurrent-neural-networks
Speech Recognition Neural Network
This is the end-to-end Speech Recognition neural network, deployed in Keras. This was my final project for Artificial Intelligence Nanodegree @Udacity.
Stars: ✭ 148 (-10.3%)
Mutual labels:  recurrent-neural-networks
Sru
SRU is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks.
Stars: ✭ 2,009 (+1117.58%)
Mutual labels:  recurrent-neural-networks
Emotion Recognition Using Speech
Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras
Stars: ✭ 159 (-3.64%)
Mutual labels:  recurrent-neural-networks
Lrp for lstm
Layer-wise Relevance Propagation (LRP) for LSTMs
Stars: ✭ 152 (-7.88%)
Mutual labels:  recurrent-neural-networks

Singing Voice Separation via Recurrent Inference and Skip-Filtering connections

Support material and source code for the method described in : S.I. Mimilakis, K. Drossos, J.F. Santos, G. Schuller, T. Virtanen, Y. Bengio, "Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask", in arXiv:1711.01437 [cs.SD], Nov. 2017. This work has been accepted for poster presentation at ICASSP 2018.

Please use the above citation if you find any of the code useful.

Listening Examples : https://js-mim.github.io/mss_pytorch/

Extensions :

  • An improvement of this work, which includes a novel regularization technique using TwinNetworks, can be found here: https://github.com/dr-costas/mad-twinnet .
  • New branch called "nmr_eval". Contains our L1 penalized model as an alternative to recurrent inference algorithm. That system was submitted to SiSEC-MUS18 and is denoted as "MDL1" & "MDLT". In addition to this, it is possible to use an additional variable, the inverse masking threshold, that can be used inside the cost function. The latter approach is ongoing work that deals with perceptual evaluation.

Requirements :

  • Numpy : numpy==1.13.1
  • SciPy : scipy==0.19.1
  • PyTorch : pytorch==0.2.0_2 (For inference and model testing pytorch==0.3.0 is supported. Training needs to be checked.)
  • TorchVision : torchvision==0.1.9
  • Other : wave(used for wav file reading), pyglet(used only for audio playback), pickle(for storing some results)
  • Trained Models : https://doi.org/10.5281/zenodo.1064805 DOI Download and place them under "results/results_inference/"
  • MIR_Eval : mir_eval=='0.4' (This is used only for unofficial cross-validation. For the reported evaluation please refer to: https://github.com/faroit/dsdtools)

Usage :

  • Clone the repository.
  • Add the base directory to your Python path.
  • While "mss_pytorch" is your current directory simply execute the "processes_scripts/main_script.py" file (iPython is prefered if the base directory was not setted up correctly.)
  • Arguments for training and testing are given inside the main function of the "processes_scripts/main_script.py" file.

Acknowledgements :

The research leading to these results has received funding from the European Union's H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement no 642685 MacSeNet.

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].