For each task there is a separate application (.py file) in applications/ directory:

applications/ DCASE2017 baseline for Task 1, Acoustic scene classification
applications/ DCASE2017 baseline for Task 2, Detection of rare sound events
applications/ DCASE2017 baseline for Task 3, Sound event detection in real life audio

Application arguments

All the usage arguments are shown by python -h.

-h, --help Application help.
-o, --overwrite Force overwrite mode.
-v, --version Show application version.

System mode

-m {dev, challenge} System mode, dev for normal development, challenge for challenge type behaviour

Each application has two operating modes: Development mode and Challenge mode. In development mode, the development dataset is used with the cross-validation setup: training applied for training set, and testing for testing set. In challenge mode, the development dataset is fully used for training the acoustic models, and a second dataset, evaluation dataset, is used for testing to generate system outputs (if ground truth is available for the evaluation dataset, the output is also evaluated). This mode is designed to be used when running the system on the evaluation dataset, for generating the system outputs for the challenge submission.

System parameters

-p FILE, --parameters FILE Parameter file (YAML) to overwrite the default parameters
-s PARAMETER_SET, --parameter_set PARAMETER_SET Parameter set id. Can be also comma separated list e.g. -s set1,set2,set3. In this case, each set is run separately.

The application supports multi-level parameter overwriting, to enable flexible switching between different system setups. The default parameters are defined in applications/parameters/task?.defaults.yaml, and these parameters are replaced by parameter set for the current run. Define here only parameters that you want to overwrite (compared to the defaults).

More about parameterization


-show_set List of available parameter sets
-show_datasets List of available datasets
-show_parameters Show current parameters
-show_eval Show evaluation results

System printing

-n, --node Node mode, console printing tuned for computer grid usage

Basic usage

With default settings, the system will download the needed datasets and extract them under directory data (storage path is controlled with parameter path->data), and proceed to train and evaluate the system, for example:


To run all provided system setups one after another:

python -s dcase2017,dcase2017_gpu,gmm,minimal

For development with the system, one should create a new parameter set file in order to overwrite the default parameters with it:

python -p custom.yaml -s custom_set

Example of custom.yaml file:

active_set: custom_set

    - set_id: custom_set
        win_length_seconds: 0.1
        hop_length_seconds: 0.5

To run the system in challenge mode:

python -p custom.yaml -s custom_set -m challenge