Submission to EUSIPCO 2023

This Repository is dedicated to the Author submission of to Eusipco 2023.

The skeleton of the code is similar to CPJKU submissions to DCASE2022 and the PaSST repository.

Setting up the Environment:

An installation of conda is required on your system.

This repo uses forked versions of sacred for configuration and logging, pytorch-lightning as a convenient pytorch wrapper and ba3l as an integrating tool between mongodb, sacred and pytorch lightning.

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To setup the environment Mamba is recommended and faster than conda:

conda install mamba -n base -c conda-forge

Now you can import the environment from environment.yml. This might take several minutes.

mamba env create -f environment.yml

Alternatively, you can also import the environment using conda:

conda env create -f environment.yml

An environment named dcase22_t1 has been created. Activate the environment:

conda activate dcase22_t1

Now install sacred, ba3l and pl-lightening:

# dependencies
pip install -e 'git+https://github.com/kkoutini/ba3l@v0.0.2#egg=ba3l'
pip install -e 'git+https://github.com/kkoutini/pytorch-lightning@v0.0.1#egg=pytorch-lightning'
pip install -e 'git+https://github.com/kkoutini/sacred@v0.0.1#egg=sacred' 

to log the files you can use pip install wandb

sample run code:

CUDA_VISIBLE_DEVICES=0 python -m experiments.dcase22.t1.congater_ex with Lgate_last lambda_scheduler=0 da_lambda=2 training_method=par wandb=1

CUDA_VISIBLE_DEVICES: Select the GPU id

Lgate_last preconfigured to use Lsigmoid activation for the ConGater

lambda_scheduler: Sets the scheduler to increase lambda

da_lambda: sets the lambda for the model

training_method: sets the training algorithm to parallel which means domain adaptation and task learning at each epoch or "post" to train task first then domain adaptation with congater

wandb: log everything to weights and biases or not