A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation
This addresses echo and noise reduction in hands-free communication systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles stereophonic acoustic echo cancellation in noisy communication systems by proposing a deep complex network that decomposes the task into linear echo cancellation and residual echo suppression stages, achieving state-of-the-art performance across various conditions.
In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end signal from the microphone signal within one step, especially in low signal-to-noise ratio scenarios. In this paper, we propose a deep complex network approach to address this issue. Specially, we decompose the stereophonic acoustic echo cancellation into two stages, including linear stereophonic acoustic echo cancellation module and residual echo suppression module, where both modules are based on deep learning architectures. A multi-frame filtering strategy is introduced to benefit the estimation of linear echo by capturing more inter-frame information. Moreover, we decouple the complex spectral mapping into magnitude estimation and complex spectrum refinement. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions.