论文标题
HGCN：语音增强的谐波门控薪酬网络
HGCN: Harmonic gated compensation network for speech enhancement
论文作者
论文摘要
通过神经网络的时频（T-F）域中的掩模处理一直是单渠道语音增强的主流之一。但是，大多数模型很难处理谐波被噪声部分掩盖的情况。为了应对这一挑战，我们提出了一个谐波封闭式补偿网络（HGCN）。我们设计了高分辨率的谐波积分频谱，以提高谐波位置预测的准确性。然后，我们将语音活动检测（VAD）和配音区域检测（VRD）添加到卷积复发网络（CRN）中以滤波谐波位置。最后，使用谐波门控机制来指导补偿模型来调整CRN的粗糙结果，以获得改进的增强结果。我们的实验表明，HGCN在社区中的多种先进方法上取得了可观的增长。
Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement. However, it is hard for most models to handle the situation when harmonics are partially masked by noise. To tackle this challenge, we propose a harmonic gated compensation network (HGCN). We design a high-resolution harmonic integral spectrum to improve the accuracy of harmonic locations prediction. Then we add voice activity detection (VAD) and voiced region detection (VRD) to the convolutional recurrent network (CRN) to filter harmonic locations. Finally, the harmonic gating mechanism is used to guide the compensation model to adjust the coarse results from CRN to obtain the refinedly enhanced results. Our experiments show HGCN achieves substantial gain over a number of advanced approaches in the community.
