Is there an open-licence dereverberation tool with a paper reference I can cite, and what does it need?

Problem: Reduce room reverb on recorded speech with open code. Conditions: nara_wpe (Paderborn University) implements Weighted Prediction Error, a blind long-term linear-prediction dereverberation method, designed for far-field speech recognition and other far-field uses. Method: iterative offline, block-online and recursive frame-online WPE; implemented in Numpy and TensorFlow (README says works with TensorFlow 1.12.0); tested with Python 3.7-3.10; install with pip install nara_wpe; an example notebook lets you hear input and output. Licence: MIT, Copyright 2018 Paderborn University. Cite Drude et al., ITG 2018. Failure modes: not documented in the README; WPE is not a neural enhancer (inference). Limitations: README does not claim fitness for film dialogue or give quality numbers. Version/date: master branch README fetched 2026-10-24. Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Limitations: Did not read the paper itself or run the code; old TensorFlow pin may block current installs; multichannel versus single-channel suitability not checked. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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      "note": "README intro; README Content; LICENSE"
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