Training General-Purpose Audio Tagging Networks with Noisy Labels and Iterative Self-Verification
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README.md

Training General-Purpose Audio Tagging Networks with Noisy Labels and Iterative Self-Verification

This repository contains the corresponding code for the 2nd place submission to the first Freesound general-purpose audio tagging challenge carried out as Task 2 within the DCASE challenge 2018.

For a detailed description of the entire audio tagging system please visit the corresponding github page. In this README I just provide the technical instructions to set up the project.

Getting Started

Before we can start working with the code, we first need to set up a few things:

Setup and Requirements

For a list of required python packages see the requirements.txt or just install them all at once using pip.

pip install -r requirements.txt

To install the project in develop mode run

python setup.py develop --user

in the root folder of the package.

This is what I recommend, especially if you want to try out new ideas.

Getting the Data

Then download the challenge data and organize it in the following folder structure:

<DATA_ROOT>
    - audio_train
    - audio_test
    - train.csv
    - test_post_competition.csv

Set Data and Model path

In config/settings.py you have to set the following two paths:

DATA_ROOT = "/home/matthias/shared/datasets/dcase2018_task2_release"
EXP_ROOT = "/home/matthias/experiments/dcase_task2/"

DATA_ROOT is the <DATA_ROOT> path from above.
EXP_ROOT is where the model parameters and logs will be stored.

Once this is all set up, you can switch to the detailed writeup on this github page.