论文标题
基于有效的多任务卷积神经网络的残留声音回声抑制
Residual acoustic echo suppression based on efficient multi-task convolutional neural network
论文作者
论文摘要
声音回波会降低语音通信系统中的用户体验，因此需要完全抑制。我们建议使用有效的卷积神经网络实时残留的声音回声抑制（RAES）方法。双对探测器被用作一项辅助任务，以在多任务学习的背景下提高RAE的性能。训练标准基于新的损失函数，我们称之为抑制损失，以平衡抑制残留回声和近端信号的失真。实验结果表明，所提出的方法可以在不同情况下有效抑制残差回波。
Acoustic echo degrades the user experience in voice communication systems thus needs to be suppressed completely. We propose a real-time residual acoustic echo suppression (RAES) method using an efficient convolutional neural network. The double talk detector is used as an auxiliary task to improve the performance of RAES in the context of multi-task learning. The training criterion is based on a novel loss function, which we call as the suppression loss, to balance the suppression of residual echo and the distortion of near-end signals. The experimental results show that the proposed method can efficiently suppress the residual echo under different circumstances.
