[fork] tifresi: Time Frequency Spectrogram Inversion

'tifresi' to be pronounced 'tifreeezy' provide a simple implementation of TF and spectrogam suitable for inversion, i.e. with a high quality phase recovery. The phase recovery algorithm used is PGHI (phase gradient heap integration).

Installation

This repository use the ltfatpy packages that requires a few libraries to be installed. The package relies on some libraries that have to be installed beforehands. To avoid any issue, please perform these steps in order and use a virtual environnement (pipenv, virtualenv or conda).

Install fftw3, lapack and cmake

On debian based unix system:

sudo apt-get install libfftw3-dev liblapack-dev cmake

On MacOS X using homebrew:

brew install fftw lapack cmake

On MacOS X using port:

sudo port install fftw-3 fftw-3-single lapack cmake

Install cython (required for installing ltfatpy):
```
pip install cython
```

Install the package from pypi

pip install tifresi

or from source

git clone https://github.com/andimarafioti/tifresi
cd tifresi
pip install .

Starting

After installation of the requirements, you can check the following notebooks:

demo.ipynb illustrates how to construct a spectrogram and invert it.
demo-mel.ipynb illustrates how to compute a mel spectrogram with the setting used in this repository.

License & citation

The content of this repository is released under the terms of the MIT license. Please consider citing our papers if you use it.

@inproceedings{marafioti2019adversarial,
  title={Adversarial Generation of Time-Frequency Features with application in audio synthesis},
  author={Marafioti, Andr{\'e}s and Perraudin, Nathana{\"e}l and Holighaus, Nicki and Majdak, Piotr},
  booktitle={International Conference on Machine Learning},
  pages={4352--4362},
  year={2019}
}

@article{pruuvsa2017noniterative,
  title={A noniterative method for reconstruction of phase from STFT magnitude},
  author={Pr{\uu}{\v{s}}a, Zden{\v{e}}k and Balazs, Peter and S{\o}ndergaard, Peter Lempel},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  volume={25},
  number={5},
  pages={1154--1164},
  year={2017},
  publisher={IEEE}
}

Developing

As a developer, you can test the package using pytest:

pip install pytest

Then run tests using

pytest tifresi

You can also use the source code checker flake8:

pip install flake8