For each task there is a separate application (.py file) in
||DCASE2017 baseline for Task 1, Acoustic scene classification|
||DCASE2017 baseline for Task 2, Detection of rare sound events|
||DCASE2017 baseline for Task 3, Sound event detection in real life audio|
All the usage arguments are shown by
python task1.py -h.
||Force overwrite mode.|
||Show application version.|
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.
||Parameter file (YAML) to overwrite the default parameters|
||Parameter set id. Can be also comma separated list e.g.
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
||List of available parameter sets|
||List of available datasets|
||Show current parameters|
||Show evaluation results|
||Node mode, console printing tuned for computer grid 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 task1.py -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 task1.py -p custom.yaml -s custom_set
active_set: custom_set sets: - set_id: custom_set feature_extractor: win_length_seconds: 0.1 hop_length_seconds: 0.5
To run the system in challenge mode:
python task1.py -p custom.yaml -s custom_set -m challenge