Dataset of reverberant speech mixtures created from CMU Arctic samples and pyroomacoustics.
The dependencies are numpy, scipy, and pyroomacoustics.
Anaconda is an easy way to install them
conda env create -f environment.yml
conda activate bss_speech_dataset
Running the following script will create the dataset and store it in the data folder.
python ./make_dataset.py config.json
The content of the dataset is described in the file data/metadata.json with following structure
channelsX: [X is the number of channels]
- [list of 100 rooms]:
- room_id: the id of the room [0-99]
- n_channels: the number of channels used [X]
- room_params: the parameters used to simulate the room
- mix_filename: the names of the files containing the mixture signals
- src_filenames: the names of the files containing isolated sources
- anechoic_filenames: the names of the files containing the isolated
sources without reverberation, but with the correct time of arrivals,
this is useful for evaluating dereverberation algorithms
- rir_filenames: the names of the files containing the room impulse responses
File naming scheme
channelsX_roomY_mix.wav
The file that contains an X-channels mixture of X sources in room Y
channelsX_roomY_micZ.wav
The file that contains X-channels with each isolated source in one of the channels
all recorded by microphone with index Z in room Y
channelsX_roomY_micZ_anechoic.wav
The file that contains X-channels with each non-reverberant isolated source in one
of the channels all recorded by microphone with index Z in room Y
rir_channelsX_roomY_micZ_srcT.wav
The file that contains the impulse response between source T and microphone Z in
room Y for the X-channels mixture