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reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T07:01:10.405Z","updated_at":"2026-02-19T08:01:39.572Z","avatar_url":"https://github.com/fgnt.png","language":"Python","funding_links":[],"categories":["Speech Separation (single channel)"],"sub_categories":["BSS/ICA method"],"readme":"# Convolutive Transfer Function Invariant SDR\n\n\n[![ISCA DOI](https://img.shields.io/badge/ICASSP/DOI-10.1109/ICASSP39728.2021.9414661-blue.svg)][DOI]\n[![arXiv](https://img.shields.io/badge/arXiv-2011.15003-b31b1b.svg)][arXiv]\n\n![Run python tests](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20tests/badge.svg)\n[![PyPI](https://img.shields.io/pypi/v/ci_sdr)](https://pypi.org/project/ci-sdr)\n[![codecov.io](https://codecov.io/github/fgnt/ci_sdr/coverage.svg?branch=main)](https://codecov.io/github/fgnt/ci_sdr?branch=main)\n[![PyPI - Downloads](https://img.shields.io/pypi/dm/ci_sdr)](https://pypi.org/project/ci-sdr)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://github.com/fgnt/ci_sdr/blob/master/LICENSE)\n\n\u003c!-- ![Run python dependency test](https://github.com/fgnt/ci_sdr/workflows/Run%20python%20dependency%20test/badge.svg) --\u003e\n\n\nThis repository contains an implementation for the `Convolutive transfer function Invariant Signal-to-Distortion Ratio` objective for PyTorch as described in the publication `Convolutive Transfer Function Invariant SDR training criteria for Multi-Channel Reverberant Speech Separation` ([IEEE ICASSP][DOI], [RIS UPB][RIS], [arXiv][arXiv]).\n\nHere, a small example, how you can use this CI-SDR objective in your own source code:\n\n```python\nimport torch\nimport ci_sdr\n\nreference: torch.tensor = ...\n# reference.shape: [speakers, samples]\n\nestimation: torch.tensor = ...\n# estimation shape: [speakers, samples]\n\nsdr = ci_sdr.pt.ci_sdr_loss(estimation, reference)\n# sdr shape: [speakers]\n```\n\nThe idea of this objective function is based in the theory from `E. Vincent, R. Gribonval and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech and Language Processing`, known as\n`BSSEval`.\nThe original author provided MATLAB source code ([link](http://bass-db.gforge.inria.fr/bss_eval/)) and the package `mir_eval` ([link](http://craffel.github.io/mir_eval/#module-mir_eval.separation)) contains a python port. Some people refer to these implementations as `BSSEval v3` ([link](https://github.com/sigsep/bsseval)).\n\nThe PyTorch code in this package is tested to yield the same `SDR` values as `mir_eval` with the default parameters.\n\n\u003e **NOTE:** If you want to use `BSSEval v3 SDR` as metric, I recomment to use `mir_eval.separation.bss_eval_sources` and use as reference the clean/unreverberated source signals. The implementation in this repository has minor difference that makes it problematic to compare SDR values accorss different publications (e.g. here the permutation is calculated on the SDR, while `mir_eval` computes it based on the `SIR`.).\n\n\n\n# Installation\n\nInstall it directly with Pip, if you just want to use it:\n\n```bash\npip install ci-sdr\n```\n\nor to get the recent version:\n\n```bash\npip install git+https://github.com/fgnt/ci_sdr.git\n```\n\nIf you want to install it with `all` dependencies (test and doctest dependencies), run:\n\n```bash\npip install git+https://github.com/fgnt/ci_sdr.git#egg=ci_sdr[all]\n```\n\nWhen you want to change the code, clone this repository and install it as `editable`:\n\n```bash\ngit clone https://github.com/fgnt/ci_sdr.git\ncd ci_sdr\npip install --editable .\n# pip install --editable .[all]\n```\n\n# Citation\n\nTo cite this implementation, you can cite the following paper ([IEEE ICASSP][DOI], [RIS UPB][RIS], [arXiv][arXiv]):\n```\n@inproceedings{boeddeker2021convolutive,\n  title={Convolutive transfer function invariant SDR training criteria for multi-channel reverberant speech separation},\n  author={Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold},\n  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},\n  pages={8428--8432},\n  year={2021},\n  organization={IEEE},\n  doi={10.1109/ICASSP39728.2021.9414661}}\n}\n```\n\n[RIS]: https://ris.uni-paderborn.de/download/28259/28260/ICASSP2021_BSSEval.pdf\n[DOI]: https://doi.org/10.1109/ICASSP39728.2021.9414661\n[arXiv]: https://arxiv.org/abs/2011.15003\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffgnt%2Fci_sdr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffgnt%2Fci_sdr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffgnt%2Fci_sdr/lists"}