论文标题
基于神经声码器的数据包损失隐藏算法
A Neural Vocoder Based Packet Loss Concealment Algorithm
论文作者
论文摘要
数据包丢失问题严重影响了IP（VOIP）场景的语音服务质量。在本文中，我们调查了基于在线接收器的数据包丢失隐藏，这更便携式和适用。为了确保语音自然性，而不是直接处理频域中的时间域波形或单独重建幅度和阶段，而是采用了基于流动的神经声码器来从历史记录内容中产生丢失的数据包的替代波形，该替代波形是由历史悠久的神经预测器产生的。此外，创建了基于波形的平滑后制品，以减轻语音的不连续性并避免伪影。实验结果表明该方法的出色性能。
The packet loss problem seriously affects the quality of service in Voice over IP (VoIP) sceneries. In this paper, we investigated online receiver-based packet loss concealment which is much more portable and applicable. For ensuring the speech naturalness, rather than directly processing time-domain waveforms or separately reconstructing amplitudes and phases in frequency domain, a flow-based neural vocoder is adopted to generate the substitution waveform of lost packet from Mel-spectrogram which is generated from history contents by a well-designed neural predictor. Furthermore, a waveform similarity-based smoothing post-process is created to mitigate the discontinuity of speech and avoid the artifacts. The experimental results show the outstanding performance of the proposed method.
