Neural network based SAD

If you want to train a neural network for speech activity detection on the ham_radio database follow these steps:

1. Clone this repository and install it with pip. (We assume that Cython and numpy are already installed)
2. Download the database with

    wget -qO- https://zenodo.org/record/5175960/files/ham_radio.tar.gz.parta{a,b,c} \
	| tar -C /PATH/TO/HAM_RADIO_DB/ -zx --checkpoint=10000 --checkpoint-action=echo="%u/5530000 %c"

where /PATH/TO/HAM_RADIO_DB has to be replaced with the chosen database directory

1. Set the variable HAM_RADIO_JSON to the file name the database json should be written to export HAM_RADIO_JSON=/PATH/TO/JSON
2. Create a database json with

python -m ham_radio.database.ham_radio.create_json \
    with database_path=/PATH/TO/HAM_RADIO_DB

1. Set a directory to which to write all models with export STORAGE_ROOT=/PATH/TO/MODEL_DIR
2. Start a training with:

   python -m ham_radio.train with cnn

The trained model and the event files are written to

  /PATH/TO/MODEL_DIR/ham_radio/SADModel_{number_of_train_runs}

For more information about the training script and the event files visit our padertorch repository

If you want to reduce the required space for the gpu you can reduce the batch size by adding provider_opts.batch_size=4 or any other value for the batch size.

If you want to use a simple RNN structure instead of the
RNN you can replace cnn with rnn Most paramters are adjustable in a similar fashion.

Citation

@misc{heitkaemper2021database,
     title={A Database for Research on Detection and Enhancement of Speech Transmitted over HF links}, 
     author={Jens Heitkaemper and Joerg Schmalenstroeer and Joerg Ullmann and Valentin Ion and Reinhold Haeb-Umbach},
     year={2021},
     eprint={2106.02472},
     archivePrefix={arXiv},
     primaryClass={cs.SD}
}