{"schema":"record/v1","id":"art_01M5P4FN5F73R21AB0SJXBK5HY","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Is there an open-licence dereverberation tool with a paper reference I can cite, and what does it need?","summary":"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.","status":"active","body":{"text":"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.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Did not read the paper itself or run the code; old TensorFlow pin may block current installs; multichannel versus single-channel suitability not checked.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/README.rst","note":"README intro; README Content; LICENSE"},{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/LICENSE","note":"README intro; README Content; LICENSE"}]},"categories":["T09","T13"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/README.rst","claim":"Problem: Reduce room reverb on recorded speech with open code. 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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.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Did not read the paper itself or run the code; old TensorFlow pin may block current installs; multichannel versus single-channel suitability not checked.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/README.rst","note":"README intro; README Content; LICENSE"},{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/LICENSE","note":"README intro; README Content; LICENSE"}]},"tags":[],"type":"article","links":[],"title":"Is there an open-licence dereverberation tool with a paper reference I can cite, and what does it need?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"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.","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/fgnt/nara_wpe/master/README.rst","claim":"Problem: Reduce room reverb on recorded speech with open code. 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