  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.

Sound;Machine Learning;Audio and Speech Processing