TC: Tensor-Train-Neural-Network

The package particularly aims at Tensor Decompositions to Deep Neural Networks, particularly for Tensor-to-Vector Regression tasks, e.g., Speech and Image Enhancement.

git clone https://github.com/uwjunqi/Tensor-Train-Neural-Network.git
cd Tensor-Train-Neural-Network

Installation

The main dependencies are h5py, Numpy and PyTorch. To download and install tc:

CUDA 10.0 setup

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
conda install -c anaconda h5py 
conda install -c conda-forge matplotlib 
python setup.py install

Tensor-Train Neural Network for Image Processing

• TTN AE result

• DNN AE result

cd image
python train_tt_image.py

Tensor-Train Neural Network for Speech Enhancement

cd speech
extract_feat.py --train_clean_list_fn="data/train_clean.scp" --train_noisy_list_fn="data/train_noisy.scp" --test_clean_list_fn="test_clean.scp" --test_noisy_list_fn="test_noisy.scp"

python train_tt_speech.py

Contributing

Pull requests are welcome!

Besides, using the issue tracker, feel free to contact me at jqi41@gatech.edu.

Paper Citation:

Note that the implementation of the PyTorch codes are not totally the same as our Tensorflow codes for the experimental setups in [1] Qi et al. ICASSP 2020 and [2] Qi et al.TASLP.

This repo is releaed for general use and also included some image TTN examples. If your are intested on reproducing the results of [1] and [2] please contact. (jqi41 at gatech dot edu)

If you use the codes for your research work, please consider cite the following paper:

[1] Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee, "Tensor-to-Vector Regression for Multi-Channel Speech Enhancement based on Tensor-Train Network,” in Proc. IEEE Intl. Conf. on Acoustic, Speech, and Signal Processing (ICASSP), Barcelona, Spain, 2020.

https://arxiv.org/abs/2002.00544

{
  @article{qi2020tensor,
  title={Tensor-to-Vector Regression for Multi-Channel Speech Enhancement based on Tensor-Train Network},
  author={Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee},
  journal={IEEE ICASSP},
  volume={},
  number={},
  pages={},
  year={2020},
  publisher={IEEE}
}

[2] Jun Qi, Jun Du, Sabato Marco Siniscalchi, Chin-Hui Lee, "A Theory on Deep Neural Network based Vector-to-Vector Regression with an Illustration of Its Expressive Power in Speech Enhancement", in IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Vol 27 , no. 12, pp. 1932-1943, Dec 2019.

  {
  @article{qi2019theory,
  title={A Theory on Deep Neural Network Based Vector-to-Vector Regression With an Illustration of Its Expressive Power in Speech Enhancement},
  author={Qi, Jun and Du, Jun and Siniscalchi, Sabato Marco and Lee, Chin-Hui},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={27},
  number={12},
  pages={1932--1943},
  year={2019},
  publisher={IEEE}
}