  Loopable music generation systems enable diverse applications, but they often
lack controllability and customization capabilities. We argue that enhancing
controllability can enrich these models, with emotional expression being a
crucial aspect for both creators and listeners. Hence, building upon LooperGP,
a loopable tablature generation model, this paper explores endowing systems
with control over conveyed emotions. To enable such conditional generation, we
propose integrating musical knowledge by utilizing multi-granular semantic and
musical features during model training and inference. Specifically, we
incorporate song-level features (Emotion Labels, Tempo, and Mode) and bar-level
features (Tonal Tension) together to guide emotional expression. Through
algorithmic and human evaluations, we demonstrate the approach's effectiveness
in producing music conveying two contrasting target emotions, happiness and
sadness. An ablation study is also conducted to clarify the contributing
factors behind our approach's results.
