Deep Learning – EAT Challenge


This project is part of an AI challenge of the DeepLearning course 2021 at the University of Augsburg.
The objective to be learned is a classification task telling which food people are eating on audio recordings.


This project was created by:

  • Benjamin Möckl
  • Julian Göser
  • Marco Tröster

EAT Dataset Setup

For your convenience, the download of all external project assets (dataset and evaluation metrics) has been
automated by a shell script. After executing the script you should be ready to run / develop the project code.

# download and unpack the dataset and metric files
./ <dataset zip password>

How to Run

First, cache the input dataset as TFRecord files for a training session (e.g. naive training).
This should massively improve your training performance (especially with low CPU / GPU resources).

# cache the preprocessed audio dataset as TFRecord file
python src/ preprocess_dataset naive

Now, you can launch a training session (e.g. naive training).

# process a training session
python src/ run_training naive

After that you can sample all inputs of the unknown test dataset using a trained model
and export the prediction results for EAT challenge submission.

# evaluate the results for submission
python src/ eval_results naive

Valid training configurations are:

  • naive
  • noisy
  • autoenc
  • amplitude

Remark: Use a GPU empowered machine for amplitude training (although it won’t be too rewarding anyways).
Tested on Ubuntu 20.04. For running on Windows, the keras ModelCheckpoint Callback has to be switched to
our SaveBestAccuracyCallback.

Training Results

Training Approach Description Test Acc. Real Acc.
Naive Train on audio melspectrograms using Conv2D 0.41 0.36
Noisy Train on audio melspectrograms using custom noisy Conv2D 0.44 0.39
Amplitude Train on audio amplitude using Conv1D 0.23 ?.??
AutoEnc Train on audio melspectrograms using an Auto Encoder 0.25 ?.??


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