SignalTrain LA2A Dataset (v.1.1)

Name: SignalTrain-LA2A
Creator: Scott Hawley
License: https://choosealicense.com/licenses/cc-by-4.0/

Downloadable from https://zenodo.org/records/3824876

20 GB of audio in & audio out for a LA-2A compressor unit, conditioned on knob variations.

LA-2A Compressor data to accompany the paper "SignalTrain: Profiling Audio Compressors with Deep Neural Networks," 147th Audio Engineering Society Convention (AES), 2019. https://arxiv.org/abs/1905.11928

Accompanying computer code: https://github.com/drscotthawley/signaltrain

A collection of recorded data from an analog Teletronix LA-2A opto-electronic compressor, for various settings of the Peak Reduction knob. Other knobs were kept constant.

Audio samples present in these files are either 'randomly generated', or downloaded audio clips with Create Commons licenses, or are property of Scott Hawley freely distributed as part of this dataset.

Data taken by Ben Colburn, supervised by Scott Hawley

Revisions in v.1.1 of dataset:

Made the following corrections to discrepancies in original dataset:

Only one of file: 235, 236

$ rm Train/target_235_LA2A_2c__0__70.wav

$ rm Val/input_236_.wav

In wrong directory: 245

$ mv Train/input_245_.wav Val/

Mismatched length and time alignment: 148, 148, 149, 150, 152

All were had targets delayed by 8583 samples relative to inputs, and were shorter.

Truncated beginning of inputs to make them the same as targets. Used new script check_dataset.py to fix & overwrite earlier version:

$ signaltrain/utils/check_dataset.py --fix SignalTrain_LA2A_Dataset/

Papers dataset was used in:

"Efficient neural networks for real-time analog audio effect modeling" by C. Steinmetz & J. Reiss, 2021. https://arxiv.org/abs/2102.06200

“Exploring quality and generalizability in parameterized neural audio effects," by W. Mitchell and S. H. Hawley, 149th Audio Engineering Society Convention (AES), 2020. https://arxiv.org/abs/2006.05584

"SignalTrain: Profiling Audio Compressors with Deep Neural Networks," 147th Audio Engineering Society Convention (AES), 2019. https://arxiv.org/abs/1905.11928