GCCaps - Capsule Routing for Sound Event Detection

This is the source code for the system described in the paper 'Capsule Routing for Sound Event Detection' <https://arxiv.org/abs/1806.04699>_.

Requirements

This software requires Python 3.5 or higher. To install the dependencies, run::

pip install -r requirements.txt

The main functionality of this software also requires the DCASE 2017 Task 4 datasets, which may be downloaded here_. After acquiring the datasets, modify gccaps/config/paths.py accordingly.

For example::

_root_dataset_path = ('/path/to/datasets')
"""str: Path to root directory containing input audio clips."""

training_set = Dataset(
    name='training',
    path=os.path.join(_root_dataset_path, 'training'),
    metadata_path='metadata/groundtruth_weak_label_training_set.csv',
)
"""Dataset instance for the training dataset."""

You may also want to change the work path::

work_path = '/path/to/workspace'
"""str: Path to parent directory containing program output."""

.. _here: http://www.cs.tut.fi/sgn/arg/dcase2017/challenge/download#task4---large-scale-weakly-supervised-sound-event-detection-for-smart-cars

Usage

In this section, the various commands are described. Using this software, the user is able to extract feature vectors, train the network, generate predictions using the trained network, and evaluate the predictions.

Feature Extraction ^^^^^^^^^^^^^^^^^^

To extract feature vectors, run::

python gccaps/main.py extract <training/validation/test>

See gccaps/config/logmel.py for tweaking the parameters.

Training ^^^^^^^^

To train the GCCaps network, run::

python gccaps/main.py train

See gccaps/config/training.py for tweaking the parameters, or gccaps/training.py for further modifications.

Prediction ^^^^^^^^^^

To generate predictions, run::

python gccaps/main.py predict [validation/test]

See gccaps/config/predictions.py to modify which models (corresponding to different epochs) are selected for generating the predictions. By default, the top five models based on their F-score on the validation set are chosen.

Evaluation ^^^^^^^^^^

To evaluate the predictions, run::

python gccaps/main.py evaluate <tagging/sed/all> [validation/test] [--thresholds]

See gccaps/config/predictions.py for tweaking the parameters.

The --thresholds flag indicates that 'optimal' class-wise probability thresholds should be computed and recorded. That is, precision-recall curves will be computed for each class, and the threshold corresponding to the highest F-score will be saved to disk. The idea is to compute the thresholds for the validation set and use them for the test set. To use the 'optimal' thresholds, set at_threshold or sed_threshold in gccaps/config/predictions.py to -1. This is included for experimental purposes and may be detrimental.