Acoustic Echo Cancellation With The Dual-signal Transformation LSTM Network

Abstract

This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC). The DTLN combines a short-time Fourier transformation and a learned feature representation in a stacked network approach, which enables robust information processing in the time-frequency and in the time domain, which also includes phase information. The model is only trained on 60~h of real and synthetic echo scenarios. The training setup includes multi-lingual speech, data augmentation, additional noise and reverberation to create a model that should generalize well to a large variety of real-world conditions. The DTLN approach produces state-of-the-art performance on clean and noisy echo conditions reducing acoustic echo and additional noise robustly. The method outperforms the AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).

Stats

• citations52
• S2 citations—
• github stars0
• HF likes0
• heat score12.93
• arxiv keywesthausen2020acoustic

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
• An Adaptive Filter Bank Based Neural Network Approach For Time Delay Estimation And Speech Enhancement (2025)0.00
• Joint Neural AEC And Beamforming With Double-talk Detection (2021)3.58
• Joint Echo Cancellation And Noise Suppression Based On Cascaded Magnitude And Complex Mask Estimation (2021)0.00
• Multi-task Deep Residual Echo Suppression With Echo-aware Loss (2022)10.74
• Deep Multi-task Network For Delay Estimation And Echo Cancellation (2020)0.00
• LSTM Based AE-DNN Constraint For Better Late Reverb Suppression In Multi-channel LP Formulation (2018)0.00