Acoustic echo cancellation
13 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Acoustic echo cancellation
Libraries
Use these libraries to find Acoustic echo cancellation models and implementationsDatasets
ICASSP 2021 Acoustic Echo Cancellation Challenge
Most implemented papers
Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet
Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and far-end signal.
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework
In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios.
Semi-Blind Source Separation for Nonlinear Acoustic Echo Cancellation
Unlike the commonly utilized adaptive algorithm, the proposed 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.
Nonlinear Residual Echo Suppression using a Recurrent Neural Network
rrbluke/NRES
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Interspeech 2020
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.
Acoustic echo cancellation with the dual-signal transformation LSTM network
breizhn/DTLN-aec
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This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC).
Acoustic Echo Cancellation with Cross-Domain Learning
rrbluke/CDEC
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Interspeech 2021
This paper proposes the Cross-Domain Echo-Controller(CDEC), submitted to the Interspeech 2021 AEC-Challenge. The algorithm consists of three building blocks: (i) a Time-Delay Compensation (TDC) module, (ii) a frequency-domainblock-based Acoustic Echo Canceler (AEC), and (iii) a Time-Domain Neural-Network (TD-NN) used as a post-processor. Our system achieves an overall MOS score of 3. 80, while onlyusing 2. 1 million parameters at a system latency of 32ms.
ICASSP 2022 Acoustic Echo Cancellation Challenge
This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recognition rate in the challenge goal metrics, and making the default sample rate 48 kHz.
Joint Acoustic Echo Cancellation and Blind Source Extraction based on Independent Vector Extraction
We describe a joint acoustic echo cancellation (AEC) and blind source extraction (BSE) approach for multi-microphone acoustic frontends.
Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation
The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo.
Low-Complexity Acoustic Echo Cancellation with Neural Kalman Filtering
fjiang9/nkf-aec
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The Kalman filter has been adopted in acoustic echo cancellation due to its robustness to double-talk, fast convergence, and good steady-state performance.
