Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling

Understand

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges.

• Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency.
• In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges.
• Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles; subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement.