Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for the AEC-Challenge
Ziteng Wang, Yueyue Na, Zhang Liu, Biao Tian, Qiang Fu
Abstract
This paper presents a real-time Acoustic Echo Cancellation (AEC) algorithm submitted to the AEC-Challenge. The algorithm consists of three modules: Generalized Cross-Correlation with PHAse Transform (GCC-PHAT) based time delay compensation, weighted Recursive Least Square (wRLS) based linear adaptive filtering and neural network based residual echo suppression. The wRLS filter is derived from a novel semi-blind source separation perspective. The neural network model predicts a Phase-Sensitive Mask (PSM) based on the aligned reference and the linear filter output. The algorithm achieved a mean subjective score of 4.00 and ranked 2nd in the AEC-Challenge.
Topics & Concepts
ResidualEcho (communications protocol)Adaptive filterArtificial neural networkComputer scienceCompensation (psychology)AlgorithmFilter (signal processing)Least mean squares filterMean squared errorLinear phaseControl theory (sociology)Artificial intelligenceMathematicsStatisticsComputer visionPsychologyPsychoanalysisComputer networkControl (management)Speech and Audio ProcessingAdvanced Adaptive Filtering TechniquesBlind Source Separation Techniques