基于Intra-BRNN与GB-RVQ的端到端神经音频编解码器
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
AI总结：
针对低比特率语音编码中帧内相关性利用不足、量化误差影响音质的问题，提出结合1D-CNN与Intra-BRNN交错结构、采用GB-RVQ的CBRC端到端神经编解码器，3kbps下性能优于12kbps的Opus，适用于实时通信。
摘要：
近年来，神经网络已被证明在低比特率语音编码任务中具备有效性。然而，帧内相关性利用不足以及量化器误差会降低重建音频的质量。为提升编码质量，我们提出一种端到端神经语音编解码器，即CBRC（卷积与双向循环神经编解码器）。我们设计了结合1D-CNN与Intra-BRNN的交错结构，以更高效地利用帧内相关性。此外，采用分组与波束搜索残差矢量量化器（GB-RVQ）来降低量化噪声。CBRC每20ms对音频编码一次，无额外延迟，适用于实时通信。实验结果表明，3kbps的CBRC相较于12kbps的Opus编解码器性能更优。
英文摘要：
Recently, neural networks have proven to be effective in performing speech coding task at low bitrates. However, under-utilization of intra-frame correlations and the error of quantizer specifically degrade the reconstructed audio quality. To improve the coding quality, we present an end-to-end neural speech codec, namely CBRC (Convolutional and Bidirectional Recurrent neural Codec). An interleaved structure using 1D-CNN and Intra-BRNN is designed to exploit the intra-frame correlations more efficiently. Furthermore, Group-wise and Beam-search Residual Vector Quantizer (GB-RVQ) is used to reduce the quantization noise. CBRC encodes audio every 20ms with no additional latency, which is suitable for real-time communication. Experimental results demonstrate the superiority of the proposed codec when comparing CBRC at 3kbps with Opus at 12kbps.
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。
