A repository for paper "A simple repository template for reproducible research based on Python recipe"

by Bagus Tris Atmaja, Masato Akagi

Email: bagus@ep.its.ac.id

This is a template for papers that use Python codes to generate their results (though it can be adapted to use other technologies). The text is written in LaTex and tasks are generated using pdflatex command. Ideally, all results, figures and the final paper PDF should be generated by running a single this command in the latex of this repository.

This paper has been accepted for publication in TENCON 2020.

This paper compares emotional song and speech from RAVDESS dataset. We evaluates different features sets, feature types (region of analysis, LLD vs. HSF), and classifiers for speech and song data.

Caption for the example figure with the main results.

Abstract

In this paper, we argue that singing voice (song) is more emotional than speech. We evaluate different features sets, feature types, and classifiers on both song and speech emotion recognition. Three feature sets: GeMAPS, pyAudioAnalysis, and LibROSA; two feature types, low-level descriptors and high-level statistical functions; and four classifiers: multilayer perceptron, LSTM, GRU, and convolution neural networks; are examined on both songand speech data with the same parameter values. The results show no remarkable difference between song and speech data on using the same method. Comparisons of two results reveal that song is more emotional than speech. In addition, high-level statistical functions of acoustic features gained higher performance than low-level descriptors in this classification task. This result strengthens the previous finding on the regression task which reported the advantage use of high-level features.

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. 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 repository:

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

or download a zip archive.

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 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:

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.

Citation

Please cite this work as:

B.T. Atmaja and M. Akagi, “A simple repository for reproducible research based on Python recipe"