{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nonlinear-residual-echo-suppression-using-a","title":"Nonlinear Residual Echo Suppression using a Recurrent Neural Network","arxiv_id":null,"date":"2020-10-25","proceeding":"Interspeech 2020 10","authors":["Lukas Pfeifenberger","Franz Pernkopf"],"abstract":"The  acoustic  front-end  of  hands-free  communication  de-vices introduces a variety of distortions to the linear echo pathbetween the loudspeaker and the microphone. While the ampli-fiers may introduce a memory-less non-linearity, mechanical vi-brations transmitted from the loudspeaker to the microphone viathe housing of the device introduce non-linarities with memory,which are much harder to compensate. These distortions signif-icantly limit the performance of linear Acoustic Echo Cancella-tion (AEC) algorithms. While there already exists a wide rangeof Residual Echo Suppressor (RES) techniques for individualuse cases, our contribution specifically aims at a low-resourceimplementation  that  is  also  real-time  capable.   The  proposedapproach is based on a small Recurrent Neural Network (RNN)which adds memory to the residual echo suppressor, enabling itto compensate both types of non-linear distortions. We evaluatethe performance of our system in terms of Echo Return Loss En-hancement (ERLE), Signal to Distortion Ratio (SDR) and WordError Rate (WER), obtained during realistic double-talk situa-tions.  Further, we compare the postfilter against a state-of-theart implementation. Finally, we analyze the numerical complex-ity of the overall system.","url_abs":"http://www.interspeech2020.org/uploadfile/pdf/Thu-1-10-6.pdf","url_pdf":"http://www.interspeech2020.org/uploadfile/pdf/Thu-1-10-6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nonlinear-residual-echo-suppression-using-a","repo_url":"https://github.com/rrbluke/NRES","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"acoustic-echo-cancellation","task_name":"Acoustic echo cancellation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}