https://github.com/bagustris/snr

SNR = Speech Naturalness Recognition

https://github.com/bagustris/snr

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SNR = Speech Naturalness Recognition

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# Paper template for the pinga-lab (*replace with paper title*)

by
Bagus Tris Atmaja
Author 2,
etc

> This is a template for papers that use Python code and Jupyter notebooks to
> generate their results (though it can be adapted to use other technologies).
> The text is written in LaTex and tasks are automated using `Makefile`s.
> Ideally, all results, figures and the final paper PDF should be generated by
> running a single `make` command in the root of this repository.

> Fill out the sections below with the information for your paper.

This paper has been submitted for publication in *Some Journal*.

> Brief description of what this paper is about (2-3 sentences). Include a
> figure as well with the main result of your paper.

![](manuscript/figures/hawaii-trend.png)

*Caption for the example figure with the main results.*


## Abstract

> Paste here the abstract.


## Software implementation

> Briefly describe the software that was written to produce the results of this
> paper.

All source code used to generate the results and figures in the paper are in
the `code` folder.
The calculations and figure generation are all run inside
[Jupyter notebooks](http://jupyter.org/).
The data used in this study is provided in `data` and the sources for the
manuscript text and figures are in `manuscript`.
Results generated by the code are saved in `results`.
See the `README.md` files in each directory for a full description.


## Getting the code

You can download a copy of all the files in this repository by cloning the
[git](https://git-scm.com/) repository:

    git clone https://github.com/bagustris/paper_template.git

or [download a zip archive](https://github.com/bagustris/paper_template).

A copy of the repository is also archived at *insert DOI here*


## Dependencies

You'll need a working Python environment to run the code.
The recommended way to set up your environment is through the
[Anaconda Python distribution](https://www.anaconda.com/download/) which
provides the `conda` package manager.
Anaconda can be installed in your user directory and does not interfere with
the system Python installation.
The required dependencies are specified in the file `requirements.txt`.

We use `pip` virtual environments to manage the project dependencies in
isolation.
Thus, you can install our dependencies without causing conflicts with your
setup (even with different Python versions).

Run the following command in the repository folder (where `environment.yml`
is located) to create a separate environment and install all required
dependencies in it:

    pip3.6 venv REPO_NAME


## Reproducing the results

Before running any code you must activate the conda environment:

    source activate REPO_NAME

To reproduce result in , run the following in order:  
```bash
```


## License

All source code is made available under a BSD 3-clause license. You can freely
use and modify the code, without warranty, so long as you provide attribution
to the authors. See `LICENSE.md` for the full license text.

The manuscript text is not open source. The authors reserve the rights to the
article content, which is currently submitted for publication in the
JOURNAL NAME.


## References

[1] https://github.com/pinga-lab/paper-template

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