适用于ICASSP 2022 DNS CHALLENGE的谐波门控补偿网络
Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE
摘要：
语音的谐波结构是抗噪声的，但是谐波仍然可能被噪声部分地掩蔽。因此，我们之前提出了一个谐波门控补偿网络（HGCN）来预测的基础上未屏蔽的谐波和处理的粗增强模块的结果，以恢复屏蔽的谐波的完整的谐波位置。此外，还利用听觉响度损失函数对网络进行训练。对于DNS Challenge，我们更新HGCN以下方面，从而生成HGCN+。首先，采用高频带模块来帮助模型处理全频带信号。第二，余弦被用来更准确地建模谐波结构。然后，双路径编码器和双路径rnn（DPRNN）的介绍，以充分利用的特点。最后，在补偿模块中用门控残差线性结构代替门控卷积，以增加频率感受野。实验结果表明，每个模块的更新都能提高模型的性能。HGCN+在宽带和全频段测试集上的性能也优于参考模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
