  Recent years have witnessed a rapid growth of large-scale language models in
the domain of music audio. Such models enable end-to-end generation of
higher-quality music, and some allow conditioned generation using text
descriptions. However, the control power of text controls on music is
intrinsically limited, as they can only describe music indirectly through
meta-data (such as singers and instruments) or high-level representations (such
as genre and emotion). We aim to further equip the models with direct and
content-based controls on innate music languages such as pitch, chords and drum
track. To this end, we contribute Coco-Mulla, a content-based control method
for music large language modeling. It uses a parameter-efficient fine-tuning
(PEFT) method tailored for Transformer-based audio models. Experiments show
that our approach achieved high-quality music generation with low-resource
semi-supervised learning, tuning with less than 4% parameters compared to the
original model and training on a small dataset with fewer than 300 songs.
Moreover, our approach enables effective content-based controls, and we
illustrate the control power via chords and rhythms, two of the most salient
features of music audio. Furthermore, we show that by combining content-based
controls and text descriptions, our system achieves flexible music variation
generation and arrangement. Our source codes and demos are available online.
