标题： 跨尺度向量量化用于可扩展神经语音编码 显示英文标题

标题： Cross-Scale Vector Quantization for Scalable Neural Speech Coding

摘要： 比特率可扩展性是实时通信中音频编码的一个理想特性。 现有的神经音频编解码器通常在训练期间强制特定的比特率，因此需要为每个目标比特率训练不同的模型，这增加了发送方和接收方的内存占用，并且通常需要转码以支持多个接收方。 在本文中，我们引入了一种跨尺度可扩展的矢量量化方案（CSVQ），其中多尺度特征通过逐步的特征融合和优化进行渐进式编码。 这样，如果只接收到一部分比特流，就可以重建粗略级别的信号，并且随着更多比特的可用，质量会逐步提高。 所提出的CSVQ方案可以灵活地应用于任何具有镜像自编码器结构的神经音频编码网络，以实现比特率可扩展性。 主观结果表明，所提出的方案在可扩展性方面优于经典的残差VQ（RVQ）。 此外，3 kbps的CSVQ在性能上超过了9 kbps的Opus和3 kbps的Lyra，并且可以在比特率增加时提供平滑的质量提升。 显示英文摘要

摘要： Bitrate scalability is a desirable feature for audio coding in real-time communications. Existing neural audio codecs usually enforce a specific bitrate during training, so different models need to be trained for each target bitrate, which increases the memory footprint at the sender and the receiver side and transcoding is often needed to support multiple receivers. In this paper, we introduce a cross-scale scalable vector quantization scheme (CSVQ), in which multi-scale features are encoded progressively with stepwise feature fusion and refinement. In this way, a coarse-level signal is reconstructed if only a portion of the bitstream is received, and progressively improves the quality as more bits are available. The proposed CSVQ scheme can be flexibly applied to any neural audio coding network with a mirrored auto-encoder structure to achieve bitrate scalability. Subjective results show that the proposed scheme outperforms the classical residual VQ (RVQ) with scalability. Moreover, the proposed CSVQ at 3 kbps outperforms Opus at 9 kbps and Lyra at 3kbps and it could provide a graceful quality boost with bitrate increase.