  We introduce Gull, a generative multifunctional audio codec. Gull is a
general purpose neural audio compression and decompression model which can be
applied to a wide range of tasks and applications such as real-time
communication, audio super-resolution, and codec language models. The key
components of Gull include (1) universal-sample-rate modeling via subband
modeling schemes motivated by recent progress in audio source separation, (2)
gain-shape representations motivated by traditional audio codecs, (3) improved
residual vector quantization modules, (4) elastic decoder network that enables
user-defined model size and complexity during inference time, (5) built-in
ability for audio super-resolution without the increase of bitrate. We compare
Gull with existing traditional and neural audio codecs and show that Gull is
able to achieve on par or better performance across various sample rates,
bitrates and model complexities in both subjective and objective evaluation
metrics.
