Spectral sub-bands do not portray the same perceptual relevance. In audio coding, it is therefore desirable to have independent control over each of the constituent bands so that bitrate assignment and signal reconstruction can be achieved efficiently. In this work, we present a novel neural audio coding network that natively supports a multi-band coding paradigm. Our model extends the idea of compressed skip connections in the U-Net-based codec, allowing for independent control over both core and high band-specific reconstructions and bit allocation. Our system reconstructs the full-band signal mainly from the condensed core-band code, therefore exploiting and showcasing its bandwidth extension capabilities to its fullest. Meanwhile, the low-bitrate high-band code helps the high-band reconstruction similarly to MPEG audio codecs' spectral bandwidth replication. MUSHRA tests show that the proposed model not only improves the quality of the core band by explicitly assigning more bits to it but retains a good quality in the high-band as well.

翻译：谱子带并不具有相同的感知重要性。因此，在音频编码中，理想的做法是对每个组成频带进行独立控制，以便能够高效地进行比特率分配和信号重构。在本工作中，我们提出了一种新颖的神经音频编码网络，该网络原生支持多频带编码范式。我们的模型扩展了基于U-Net编解码器中压缩跳跃连接的思想，允许对核心频带和高频带的重构及比特分配进行独立控制。我们的系统主要从压缩的核心频带码中重构全频带信号，从而充分开发并展示了其带宽扩展能力。同时，低比特率的高频带码有助于高频带重构，类似于MPEG音频编解码器的频谱带宽复制。MUSHRA测试表明，所提出的模型不仅通过显式分配更多比特来提升核心频带的质量，而且在高频带中也保持了良好的质量。