论文标题
ICASSP 2022 DNS挑战的谐波门控薪酬网络加上
Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE
论文作者
论文摘要
语音的谐波结构对噪声具有抵抗力，但谐波仍可能被噪声部分掩盖。因此，我们先前提出了一个谐波封闭式补偿网络（HGCN），以根据未掩盖的谐波预测完整的谐波位置，并处理一个粗大的增强模块的结果，以恢复掩盖的谐波。此外，听觉响度损失函数用于训练网络。对于DNS挑战，我们以以下方面更新HGCN，从而导致HGCN+。首先，使用高频模块来帮助模型处理全带信号。其次，余弦用于更准确地对谐波结构进行建模。然后，引入了双路径编码器和双路径RNN（DPRNN），以充分利用这些功能。最后，封闭的残余线性结构取代了补偿模块中的门控卷积，以增加频率的接受场。实验结果表明，每个更新的模块都会改善该模型的性能。 HGCN+还优于宽波段和全频段测试集上的参考模型。
The harmonic structure of speech is resistant to noise, but the harmonics may still be partially masked by noise. Therefore, we previously proposed a harmonic gated compensation network (HGCN) to predict the full harmonic locations based on the unmasked harmonics and process the result of a coarse enhancement module to recover the masked harmonics. In addition, the auditory loudness loss function is used to train the network. For the DNS Challenge, we update HGCN with the following aspects, resulting in HGCN+. First, a high-band module is employed to help the model handle full-band signals. Second, cosine is used to model the harmonic structure more accurately. Then, the dual-path encoder and dual-path rnn (DPRNN) are introduced to take full advantage of the features. Finally, a gated residual linear structure replaces the gated convolution in the compensation module to increase the receptive field of frequency. The experimental results show that each updated module brings performance improvement to the model. HGCN+ also outperforms the referenced models on both wide-band and full-band test sets.
