Full description of the method along with obtained results can be found at the corresponding paper. The paper is submitted to the Detection and Classification of Acoustic Scenes and Events workshop (DCASE) 2018.
Pre-trained weights together with extracted features and labels are at
This project is implemented using PyTorch.
The ADDA method, which our method is based upon, can be found here.
To use the AUDASC code, you can clone this repository,
setup the paths in the data_pre_processing/feature_params.yml file, and
install the dependencies.
Then you can choose either to do the whole procedure again (i.e. extract the features from
the audio files, pre-process the features, and apply the AUDASC method) or you can just use
our pre-extracted features (located at
Below you can find simple directions for any of the above steps. If there are any questions, please do not hesitate to communicate with us through the issues section of this repository.
If you are going to use the feature extraction part of this repository you need the following libraries:
librosa>=0.6.2
pysoundfile>=0.9.0
scikit-learn>=0.19.1
scipy>=1.1.0
numpy>=1.14.5
pandas>=0.23.4
yaml>=0.1.7
To run the core method, following is necessary:
torch>=0.4.0
yaml>=0.1.7
matplotlib>=2.2.2
- To extract the features, you can use
data_pre_processing/feature_extractor.py. - To prepare the features and labels as input to the model, you can use data_pre_processing/data_preprocessing.py
To change the setup of both files, you can use data_pre_processing/feature_params.yml
The setup of pre-training, adaptation, and test can be assigned and changed using scripts/learning_params.yml
To train the model on source domain data and adapt the pre-trained model to target domain dataset, you need to run
scripts/training.py
To test the adapted as well as non adapted model on source and target data, you simply need to run
scripts/test.py