Gull: A Generative Multifunctional Audio Codec
Yi Luo Jianwei Yu Hangting Chen Rongzhi Gu Chao Weng
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 for simpler training, (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.