medical-9

Acoustic echo cancellation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in acoustic-echo-cancellation-18

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Libraries

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Use these libraries to find acoustic-echo-cancellation-18 models and implementations

microsoft/AEC-Challenge

3 papers 351

Datasets

ICASSP 2021 Acoustic Echo Cancellation Challenge

INTERSPEECH 2021 Acoustic Echo Cancellation Challenge

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Most implemented papers

Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet

Hongsheng Chen, Teng Xiang, Kai-Jyun Chen, Jing Lu•Thu May 14 2020

Both the residual signal of the linear acoustic echo cancellation system, and the output of the adaptive filter are adopted to form multiple streams for the Conv-TasNet, resulting in more effective echo suppression while keeping a lower latency of the whole system.

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ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework

Tanel Pärnamaa, K. Sridhar, Ross Cutler, R. Aichner, Sebastian Braun, Sriram Srinivasan, H. Gamper, Ando Saabas•Wed Sep 09 2020

This work opens source two large datasets to train AEC models under both single talk and double talk scenarios, and opens source an online subjective test framework based on ITU-T P.808 for researchers to quickly test their results.

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Nonlinear Residual Echo Suppression Using a Recurrent Neural Network

Lukas Pfeifenberger, F. Pernkopf•Sat Oct 24 2020

The proposed approach is based on a small Recurrent Neural Network (RNN) which adds memory to the residual echo suppressor, enabling it to compensate both types of non-linear distortions.

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Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation

Hongsheng Chen, Jing Lu, Guoliang Cheng, Lele Liao•Sat Oct 24 2020

The proposed semi-blind source separation (SBSS) is based on the independence between the near-end signal and the reference signals, and is less sensitive to the mismatch of nonlinearity between the numerical and actual models.

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Acoustic Echo Cancellation with the Dual-Signal Transformation LSTM Network

Nils L. Westhausen, B. Meyer•Mon Oct 26 2020

The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly and outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).

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AEC in A Netshell: on Target and Topology Choices for FCRN Acoustic Echo Cancellation

T. Fingscheidt, Jan Franzen, Ernst Seidel•Mon Mar 15 2021

This work proposes for the first time—to the best of the knowledge—a pure DNN AEC in the form of an echo estimator, that is based on a competitive FCRN structure and delivers a quality useful for practical applications.

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Acoustic Echo Cancellation with Cross-Domain Learning

Lukas Pfeifenberger, F. Pernkopf, Matthias Zöhrer•Sun Aug 29 2021

This paper proposes the Cross-Domain Echo-Controller (CDEC), submitted to the Interspeech 2021 AEC-Challenge, which achieves an overall MOS score of 3.80, while only using 2.1 million parameters at a system latency of 32ms.

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ICASSP 2022 Acoustic Echo Cancellation Challenge

Tanel Pärnamaa, Ross Cutler, R. Aichner, Sebastian Braun, H. Gamper, Ando Saabas, Marju Purin, K. Sørensen•Sat Feb 26 2022

This work enhances the ICASSP 2022 Acoustic Echo Cancellation Challenge by including mobile scenarios, adding speech recognition word accuracy rate as a metric, and making the audio 48 kHz.

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Joint Acoustic Echo Cancellation and Blind Source Extraction Based on Independent Vector Extraction

Walter Kellermann, Thomas Haubner, Zbynvek Koldovsk'y•Thu May 12 2022

Evaluation with simulated acoustic data confirms the benefit of the proposed joint AEC and beamforming filter estimation in comparison to updating both filters individually.

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Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation

Kai-Jyun Chen, Jing Lu, Guoliang Cheng, Lele Liao, Yuxiang Hu, Changbao Zhu•Sun Jul 03 2022

The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications, especially in highly reverberant environments, and the natural gradient makes it hard to balance well between fast convergence speed and stability. In this paper, two more effective SBSS methods based on auxiliary-function-based independent vector analysis (AuxIVA) and independent low-rank matrix analysis (ILRMA) are proposed. The convolutive transfer function approximation is used instead of the MTF so that a long impulse response can be modeled with a short latency. The optimization schemes used in AuxIVA and ILRMA are carefully regularized according to the constrained demixing matrix of NAEC. The experimental results validate significantly better echo cancellation performances of the proposed methods.

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