we use pre-computed features & model architecture used in 3 previous papers
these are all unsupervised domain adaptation methods
Mezza, A. I., Habets, E. A. P., Müller, M., & Sarti, A. (2021).
#Unsupervised domain adaptation for acoustic scene classification
using band-wise statistics matching. Proceedings of the European
Signal Processing Conference (EUSIPCO), 11–15.
https://doi.org/10.23919/Eusipco47968.2020.9287533"
Drossos, K., Magron, P., & Virtanen, T. (2019). Unsupervised Adversarial Domain Adaptation based
on the Wasserstein Distance for Acoustic Scene Classification. Proceedings of the IEEE Workshop
on Applications of Signal Processing to Audio and Acoustics (WASPAA), 259–263. New Paltz, NY, USA.
Gharib, S., Drossos, K., Emre, C., Serdyuk, D., & Virtanen, T. (2018). Unsupervised Adversarial Domain
Adaptation for Acoustic Scene Classification. Proceedings of the Detection and Classification of
Acoustic Scenes and Events (DCASE). Surrey, UK.
Files
configs.py - Training configurations (C0 ... C3M)
generator.py - Data generator
losses.py - Loss implementations
model.py - Function to create dual-input / dual-output model
model_kaggle.py - reference CNN model from related work for acoustic scene classification (ASC)
normalization.py - Normalization methods (see Mezza et al. above)
params.py - General parameters
prediction.py - Prediction script to evaluate models on test data
training.py - Script to run the model training for 6 different configurations (see Fig. 2 in
the paper)
How to run
create python environment (e.g. with conda), the following versions were used during the paper preparation process