Reproducibility Package for Temporal Convolutional Network-Based Analog Compressor Modeling

This Zenodo record provides the reproducibility assets associated with the study: The Impact of Converter-Induced Temporal Effects on Temporal Convolutional Network-Based Modeling of an Analog Compressor (unpublished manuscript). This record is separate from the previously released training dataset and contains the supplementary materials required to reproduce model training, inference, objective evaluation, and perceptual listening experiments reported in the study. Contents include: 1. Best model checkpoints- 15 min Model A: epoch_146.pt, epoch_146.pth- 15 min Model B: epoch_141.pt, epoch_141.pth- 20 min Model A: epoch_150.pt, epoch_150.pth- 20 min Model B: epoch_147.pt, epoch_147.pth 2. MUSHRA listening-test audio files- 15_min_mushra_wav- 20_min_mushra_wav Each folder contains the WAV stimuli used in the listening tests, including:- hidden anchors band-limited at 3.5 kHz and 7 kHz- Model A outputs- Model B outputs- original hardware reference signals The stimuli are organized by source material (bass, drum, acoustic guitar, vocal), training duration (15 min or 20 min), and compression condition. 3. Training code and logs- 15 min model A training log.csv- 15 min model B training log.csv- 20 min model A training log.csv- 20 min model B training log.csv- Training code example.py The training script is provided as an example implementation, with filenames and save paths adapted to the experiment setup. 4. Listening-test spreadsheet- mushra_test.xlsx 5. Offline inference script- SA2AclonewithTCN.py This script can be used for offline WAV-to-WAV inference with `.pth` checkpoints. 6. Real-time plugin supportThe exported TorchScript `.pt` files can be loaded into the real-time SA-2A plugin available at:https://github.com/JYKlabs/SA-2A-TorchScript-Plugin The `.pth` checkpoint files can be loaded into the offline PyTorch inference application available at:https://github.com/JYKlabs/SA2A-clone-with-TCN-and-python Related dataset The aligned training dataset used to train these models is released separately at DOI: 10.5281/zenodo.19720792 Notes This Zenodo record contains research artifacts and supplementary materials rather than the training dataset itself.If you use these materials, please cite this Zenodo record and the associated article once published.