Joint Echo Cancellation And Noise Suppression Based On Cascaded Magnitude And Complex Mask Estimation

Abstract

Acoustic echo and background noise can seriously degrade the intelligibility of speech. In practice, echo and noise suppression are usually treated as two separated tasks and can be removed with various digital signal processing (DSP) and deep learning techniques. In this paper, we propose a new cascaded model, magnitude and complex temporal convolutional neural network (MC-TCN), to jointly perform acoustic echo cancellation and noise suppression with the help of adaptive filters. The MC-TCN cascades two separation cores, which are used to extract robust magnitude spectra feature and to enhance magnitude and phase simultaneously. Experimental results reveal that the proposed method can achieve superior performance by removing both echo and noise in real-time. In terms of DECMOS, the subjective test shows our method achieves a mean score of 4.41 and outperforms the INTERSPEECH2021 AEC-Challenge baseline by 0.54.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyshu2021joint

Related papers

• F-T-LSTM Based Complex Network For Joint Acoustic Echo Cancellation And Speech Enhancement (2021)11.19
• Neuralecho: A Self-attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement (2022)0.00
• Multi-task Deep Residual Echo Suppression With Echo-aware Loss (2022)10.74
• Joint Neural AEC And Beamforming With Double-talk Detection (2021)3.58
• Deep Multi-task Network For Delay Estimation And Echo Cancellation (2020)0.00
• Deepvqe: Real Time Deep Voice Quality Enhancement For Joint Acoustic Echo Cancellation, Noise Suppression And Dereverberation (2023)0.00
• Deep Residual Echo Suppression And Noise Reduction: A Multi-input FCRN Approach In A Hybrid Speech Enhancement System (2021)8.09
• Deep Interaction Between Masking And Mapping Targets For Single-channel Speech Enhancement (2021)0.00