SBSS Documentation

The Scalable BSS Toolkit (SBSS) provides end-to-end components for blind source separation research, including neural full-rank spatial covariance analysis (neural FCA/FastFCA), dataset recipes, and reusable PyTorch Lightning modules.

pip install git+https://github.com/b-sigpro/sbss

Key Features

Reproducible Recipes

End-to-end recipes document every stage (data prep, training, inference, evaluation) for reproducible studies.

HPC Ready

Recipes and Makefiles tuned for ABCI, TSUBAME, and other clusters, yet still runnable on a single workstation.

Highly Modular

Lightning tasks, common datasets, models, and utilities built to swap components and run ablations with minimal friction.

Acknowledgments

• Part of this work was developed under a commissioned project of the New Energy and Industrial Technology Development Organization (NEDO).

• Part of this software was developed by using ABCI 3.0 provided by AIST and AIST Solutions.

• Part of this software was developed by using the TSUBAME4.0 supercomputer at Institute of Science Tokyo.